From 09494ea545738538f9db2dceeffe10d421060ee5 Mon Sep 17 00:00:00 2001 From: Vincent Quenneville-Belair Date: Thu, 16 Apr 2020 15:01:14 -0400 Subject: [PATCH 01/19] set version. --- .circleci/config.yml | 4 ++-- .circleci/config.yml.in | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/.circleci/config.yml b/.circleci/config.yml index b4fd761c00..b62bb37c12 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -15,11 +15,11 @@ binary_common: &binary_common build_version: description: "version number of release binary; by default, build a nightly" type: string - default: "" + default: "0.5.0" pytorch_version: description: "PyTorch version to build against; by default, use a nightly" type: string - default: "" + default: "1.5.0" # Don't edit these python_version: description: "Python version to build against (e.g., 3.8)" diff --git a/.circleci/config.yml.in b/.circleci/config.yml.in index f5f985c982..fb3316969a 100644 --- a/.circleci/config.yml.in +++ b/.circleci/config.yml.in @@ -15,11 +15,11 @@ binary_common: &binary_common build_version: description: "version number of release binary; by default, build a nightly" type: string - default: "" + default: "0.5.0" pytorch_version: description: "PyTorch version to build against; by default, use a nightly" type: string - default: "" + default: "1.5.0" # Don't edit these python_version: description: "Python version to build against (e.g., 3.8)" From 4f76758408c346bf3f4b3f1ac606f2b75753cd7c Mon Sep 17 00:00:00 2001 From: cpuhrsch Date: Tue, 28 Apr 2020 16:58:21 -0400 Subject: [PATCH 02/19] Re-sync with internal repository (#592) --- .circleci/config.yml | 597 ------------------ .circleci/config.yml.in | 263 -------- .circleci/docker/Dockerfile | 36 -- .circleci/regenerate.py | 124 ---- .circleci/test/test_sort_yaml.py | 14 - .clang-format | 88 --- .clang-tidy | 33 - .flake8 | 4 - .github/ISSUE_TEMPLATE/bug-report.md | 53 -- .github/ISSUE_TEMPLATE/documentation.md | 9 - .github/ISSUE_TEMPLATE/feature-request.md | 24 - .../ISSUE_TEMPLATE/questions-help-support.md | 13 - .gitignore | 123 ---- .python3 | 0 .travis.yml | 36 -- 15 files changed, 1417 deletions(-) delete mode 100644 .circleci/config.yml delete mode 100644 .circleci/config.yml.in delete mode 100644 .circleci/docker/Dockerfile delete mode 100755 .circleci/regenerate.py delete mode 100755 .circleci/test/test_sort_yaml.py delete mode 100644 .clang-format delete mode 100644 .clang-tidy delete mode 100644 .flake8 delete mode 100644 .github/ISSUE_TEMPLATE/bug-report.md delete mode 100644 .github/ISSUE_TEMPLATE/documentation.md delete mode 100644 .github/ISSUE_TEMPLATE/feature-request.md delete mode 100644 .github/ISSUE_TEMPLATE/questions-help-support.md delete mode 100644 .gitignore create mode 100644 .python3 delete mode 100644 .travis.yml diff --git a/.circleci/config.yml b/.circleci/config.yml deleted file mode 100644 index b62bb37c12..0000000000 --- a/.circleci/config.yml +++ /dev/null @@ -1,597 +0,0 @@ -version: 2.1 - -# How to test the Linux jobs: -# - Install CircleCI local CLI: https://circleci.com/docs/2.0/local-cli/ -# - circleci config process .circleci/config.yml > gen.yml && circleci local execute -c gen.yml --job binary_linux_wheel_py3.8 -# - Replace binary_linux_wheel_py3.8 with the name of the job you want to test. -# Job names are 'name:' key. - -orbs: - win: circleci/windows@2.0.0 - -binary_common: &binary_common - parameters: - # Edit these defaults to do a release - build_version: - description: "version number of release binary; by default, build a nightly" - type: string - default: "0.5.0" - pytorch_version: - description: "PyTorch version to build against; by default, use a nightly" - type: string - default: "1.5.0" - # Don't edit these - python_version: - description: "Python version to build against (e.g., 3.8)" - type: string - unicode_abi: - description: "Python 2.7 wheel only: whether or not we are cp27mu (default: no)" - type: string - default: "" - environment: - PYTHON_VERSION: << parameters.python_version >> - BUILD_VERSION: << parameters.build_version >> - PYTORCH_VERSION: << parameters.pytorch_version >> - UNICODE_ABI: << parameters.unicode_abi >> - CU_VERSION: cpu - -smoke_test_common: &smoke_test_common - <<: *binary_common - docker: - - image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/torchaudio/smoke_test:d26af7d0-8458-face-book-84239b5c75c0 - aws_auth: - aws_access_key_id: ${ECR_AWS_ACCESS_KEY} - aws_secret_access_key: ${ECR_AWS_SECRET_ACCESS_KEY} - -jobs: - circleci_consistency: - docker: - - image: circleci/python:3.8 - steps: - - checkout - - run: - command: | - pip install --user --progress-bar off jinja2 pyyaml - python .circleci/regenerate.py - git diff --exit-code || (echo ".circleci/config.yml not in sync with config.yml.in! Run .circleci/regenerate.py to update config"; exit 1) - - binary_linux_wheel: - <<: *binary_common - docker: - - image: "pytorch/manylinux-cuda100" - resource_class: 2xlarge+ - steps: - - checkout - - run: packaging/build_wheel.sh - - store_artifacts: - path: dist - - persist_to_workspace: - root: dist - paths: - - "*" - - binary_linux_conda: - <<: *binary_common - docker: - - image: "pytorch/conda-cuda" - resource_class: 2xlarge+ - steps: - - checkout - - run: packaging/build_conda.sh - - store_artifacts: - path: /opt/conda/conda-bld/linux-64 - - persist_to_workspace: - root: /opt/conda/conda-bld/linux-64 - paths: - - "*" - - binary_win_conda: - <<: *binary_common - executor: - name: win/default - shell: bash.exe - steps: - - checkout - - run: - command: | - choco install miniconda3 - (& "C:\tools\miniconda3\Scripts\conda.exe" "shell.powershell" "hook") | Out-String | Invoke-Expression - conda activate base - conda install -yq conda-build "conda-package-handling!=1.5.0" - bash packaging/build_conda.sh - shell: powershell.exe - - binary_macos_wheel: - <<: *binary_common - macos: - xcode: "9.0" - steps: - - checkout - - run: - # Cannot easily deduplicate this as source'ing activate - # will set environment variables which we need to propagate - # to build_wheel.sh - command: | - curl -o conda.sh https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh - sh conda.sh -b - source $HOME/miniconda3/bin/activate - packaging/build_wheel.sh - - store_artifacts: - path: dist - - persist_to_workspace: - root: dist - paths: - - "*" - - binary_macos_conda: - <<: *binary_common - macos: - xcode: "9.0" - steps: - - checkout - - run: - command: | - curl -o conda.sh https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh - sh conda.sh -b - source $HOME/miniconda3/bin/activate - conda install -yq conda-build - packaging/build_conda.sh - - store_artifacts: - path: /Users/distiller/miniconda3/conda-bld/osx-64 - - persist_to_workspace: - root: /Users/distiller/miniconda3/conda-bld/osx-64 - paths: - - "*" - - # Requires org-member context - binary_conda_upload: - docker: - - image: continuumio/miniconda - steps: - - attach_workspace: - at: ~/workspace - - run: - command: | - # Prevent credential from leaking - conda install -yq anaconda-client - set -x - anaconda -t "${CONDA_PYTORCHBOT_TOKEN}" upload ~/workspace/*.tar.bz2 -u pytorch-nightly --label main --no-progress --force - - # Requires org-member context - binary_wheel_upload: - docker: - - image: circleci/python:3.8 - steps: - - attach_workspace: - at: ~/workspace - - checkout - - run: - command: | - pip install --user awscli - export PATH="$HOME/.local/bin:$PATH" - # Prevent credential from leaking - set +x - export AWS_ACCESS_KEY_ID="${PYTORCH_BINARY_AWS_ACCESS_KEY_ID}" - export AWS_SECRET_ACCESS_KEY="${PYTORCH_BINARY_AWS_SECRET_ACCESS_KEY}" - set -x - for pkg in ~/workspace/*.whl; do - aws s3 cp "$pkg" "s3://pytorch/whl/nightly/" --acl public-read - done - - smoke_test_linux_conda: - <<: *smoke_test_common - steps: - - attach_workspace: - at: ~/workspace - - run: - name: install binaries - command: | - set -x - source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION} - conda install -v -y -c pytorch-nightly pytorch - conda install -v -y $(ls ~/workspace/torchaudio*.tar.bz2) - - run: - name: smoke test - command: | - source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION} - python -c "import torchaudio" - - smoke_test_linux_pip: - <<: *smoke_test_common - steps: - - attach_workspace: - at: ~/workspace - - run: - name: install binaries - command: | - set -x - source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION} - pip install $(ls ~/workspace/torchaudio*.whl) -f https://download.pytorch.org/whl/nightly/torch_nightly.html - - run: - name: smoke test - command: | - source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION} - python -c "import torchaudio" - - docker_image_build: - machine: - image: ubuntu-1604:201903-01 - resource_class: large - environment: - image_name: torchaudio/smoke_test - steps: - - checkout - - run: - name: build_docker image - no_output_timeout: "1h" - command: | - cd .circleci/docker && docker build . -t ${image_name}:${CIRCLE_WORKFLOW_ID} - - run: - name: upload docker image - no_output_timeout: "1h" - command: | - set +x - export AWS_ACCESS_KEY_ID=${ECR_AWS_ACCESS_KEY} - export AWS_SECRET_ACCESS_KEY=${ECR_AWS_SECRET_ACCESS_KEY} - eval $(aws ecr get-login --region us-east-1 --no-include-email) - set -x - docker tag ${image_name}:${CIRCLE_WORKFLOW_ID} 308535385114.dkr.ecr.us-east-1.amazonaws.com/${image_name}:${CIRCLE_WORKFLOW_ID} - docker push 308535385114.dkr.ecr.us-east-1.amazonaws.com/${image_name}:${CIRCLE_WORKFLOW_ID} - -workflows: - build: - jobs: - - circleci_consistency - - binary_linux_wheel: - name: binary_linux_wheel_py3.5 - python_version: '3.5' - - binary_linux_wheel: - name: binary_linux_wheel_py3.6 - python_version: '3.6' - - binary_linux_wheel: - name: binary_linux_wheel_py3.7 - python_version: '3.7' - - binary_linux_wheel: - name: binary_linux_wheel_py3.8 - python_version: '3.8' - - binary_macos_wheel: - name: binary_macos_wheel_py3.5 - python_version: '3.5' - - binary_macos_wheel: - name: binary_macos_wheel_py3.6 - python_version: '3.6' - - binary_macos_wheel: - name: binary_macos_wheel_py3.7 - python_version: '3.7' - - binary_macos_wheel: - name: binary_macos_wheel_py3.8 - python_version: '3.8' - - binary_linux_conda: - name: binary_linux_conda_py3.5 - python_version: '3.5' - - binary_linux_conda: - name: binary_linux_conda_py3.6 - python_version: '3.6' - - binary_linux_conda: - name: binary_linux_conda_py3.7 - python_version: '3.7' - - binary_linux_conda: - name: binary_linux_conda_py3.8 - python_version: '3.8' - - binary_macos_conda: - name: binary_macos_conda_py3.5 - python_version: '3.5' - - binary_macos_conda: - name: binary_macos_conda_py3.6 - python_version: '3.6' - - binary_macos_conda: - name: binary_macos_conda_py3.7 - python_version: '3.7' - - binary_macos_conda: - name: binary_macos_conda_py3.8 - python_version: '3.8' - - binary_win_conda: - name: torchaudio_win_py3.6 - python_version: "3.6" - - nightly: - jobs: - - circleci_consistency - - binary_linux_wheel: - filters: - branches: - only: nightly - name: nightly_binary_linux_wheel_py3.5 - python_version: '3.5' - - binary_wheel_upload: - context: org-member - filters: - branches: - only: nightly - name: nightly_binary_linux_wheel_py3.5_upload - requires: - - nightly_binary_linux_wheel_py3.5 - - smoke_test_linux_pip: - filters: - branches: - only: nightly - name: nightly_binary_linux_wheel_py3.5_smoke_test_pip - python_version: '3.5' - requires: - - nightly_binary_linux_wheel_py3.5_upload - - binary_linux_wheel: - filters: - branches: - only: nightly - name: nightly_binary_linux_wheel_py3.6 - python_version: '3.6' - - binary_wheel_upload: - context: org-member - filters: - branches: - only: nightly - name: nightly_binary_linux_wheel_py3.6_upload - requires: - - nightly_binary_linux_wheel_py3.6 - - smoke_test_linux_pip: - filters: - branches: - only: nightly - name: nightly_binary_linux_wheel_py3.6_smoke_test_pip - python_version: '3.6' - requires: - - nightly_binary_linux_wheel_py3.6_upload - - binary_linux_wheel: - filters: - branches: - only: nightly - name: nightly_binary_linux_wheel_py3.7 - python_version: '3.7' - - binary_wheel_upload: - context: org-member - filters: - branches: - only: nightly - name: nightly_binary_linux_wheel_py3.7_upload - requires: - - nightly_binary_linux_wheel_py3.7 - - smoke_test_linux_pip: - filters: - branches: - only: nightly - name: nightly_binary_linux_wheel_py3.7_smoke_test_pip - python_version: '3.7' - requires: - - nightly_binary_linux_wheel_py3.7_upload - - binary_linux_wheel: - filters: - branches: - only: nightly - name: nightly_binary_linux_wheel_py3.8 - python_version: '3.8' - - binary_wheel_upload: - context: org-member - filters: - branches: - only: nightly - name: nightly_binary_linux_wheel_py3.8_upload - requires: - - nightly_binary_linux_wheel_py3.8 - - smoke_test_linux_pip: - filters: - branches: - only: nightly - name: nightly_binary_linux_wheel_py3.8_smoke_test_pip - python_version: '3.8' - requires: - - nightly_binary_linux_wheel_py3.8_upload - - binary_macos_wheel: - filters: - branches: - only: nightly - name: nightly_binary_macos_wheel_py3.5 - python_version: '3.5' - - binary_wheel_upload: - context: org-member - filters: - branches: - only: nightly - name: nightly_binary_macos_wheel_py3.5_upload - requires: - - nightly_binary_macos_wheel_py3.5 - - binary_macos_wheel: - filters: - branches: - only: nightly - name: nightly_binary_macos_wheel_py3.6 - python_version: '3.6' - - binary_wheel_upload: - context: org-member - filters: - branches: - only: nightly - name: nightly_binary_macos_wheel_py3.6_upload - requires: - - nightly_binary_macos_wheel_py3.6 - - binary_macos_wheel: - filters: - branches: - only: nightly - name: nightly_binary_macos_wheel_py3.7 - python_version: '3.7' - - binary_wheel_upload: - context: org-member - filters: - branches: - only: nightly - name: nightly_binary_macos_wheel_py3.7_upload - requires: - - nightly_binary_macos_wheel_py3.7 - - binary_macos_wheel: - filters: - branches: - only: nightly - name: nightly_binary_macos_wheel_py3.8 - python_version: '3.8' - - binary_wheel_upload: - context: org-member - filters: - branches: - only: nightly - name: nightly_binary_macos_wheel_py3.8_upload - requires: - - nightly_binary_macos_wheel_py3.8 - - binary_linux_conda: - filters: - branches: - only: nightly - name: nightly_binary_linux_conda_py3.5 - python_version: '3.5' - - binary_conda_upload: - context: org-member - filters: - branches: - only: nightly - name: nightly_binary_linux_conda_py3.5_upload - requires: - - nightly_binary_linux_conda_py3.5 - - smoke_test_linux_conda: - filters: - branches: - only: nightly - name: nightly_binary_linux_conda_py3.5_smoke_test_conda - python_version: '3.5' - requires: - - nightly_binary_linux_conda_py3.5_upload - - binary_linux_conda: - filters: - branches: - only: nightly - name: nightly_binary_linux_conda_py3.6 - python_version: '3.6' - - binary_conda_upload: - context: org-member - filters: - branches: - only: nightly - name: nightly_binary_linux_conda_py3.6_upload - requires: - - nightly_binary_linux_conda_py3.6 - - smoke_test_linux_conda: - filters: - branches: - only: nightly - name: nightly_binary_linux_conda_py3.6_smoke_test_conda - python_version: '3.6' - requires: - - nightly_binary_linux_conda_py3.6_upload - - binary_linux_conda: - filters: - branches: - only: nightly - name: nightly_binary_linux_conda_py3.7 - python_version: '3.7' - - binary_conda_upload: - context: org-member - filters: - branches: - only: nightly - name: nightly_binary_linux_conda_py3.7_upload - requires: - - nightly_binary_linux_conda_py3.7 - - smoke_test_linux_conda: - filters: - branches: - only: nightly - name: nightly_binary_linux_conda_py3.7_smoke_test_conda - python_version: '3.7' - requires: - - nightly_binary_linux_conda_py3.7_upload - - binary_linux_conda: - filters: - branches: - only: nightly - name: nightly_binary_linux_conda_py3.8 - python_version: '3.8' - - binary_conda_upload: - context: org-member - filters: - branches: - only: nightly - name: nightly_binary_linux_conda_py3.8_upload - requires: - - nightly_binary_linux_conda_py3.8 - - smoke_test_linux_conda: - filters: - branches: - only: nightly - name: nightly_binary_linux_conda_py3.8_smoke_test_conda - python_version: '3.8' - requires: - - nightly_binary_linux_conda_py3.8_upload - - binary_macos_conda: - filters: - branches: - only: nightly - name: nightly_binary_macos_conda_py3.5 - python_version: '3.5' - - binary_conda_upload: - context: org-member - filters: - branches: - only: nightly - name: nightly_binary_macos_conda_py3.5_upload - requires: - - nightly_binary_macos_conda_py3.5 - - binary_macos_conda: - filters: - branches: - only: nightly - name: nightly_binary_macos_conda_py3.6 - python_version: '3.6' - - binary_conda_upload: - context: org-member - filters: - branches: - only: nightly - name: nightly_binary_macos_conda_py3.6_upload - requires: - - nightly_binary_macos_conda_py3.6 - - binary_macos_conda: - filters: - branches: - only: nightly - name: nightly_binary_macos_conda_py3.7 - python_version: '3.7' - - binary_conda_upload: - context: org-member - filters: - branches: - only: nightly - name: nightly_binary_macos_conda_py3.7_upload - requires: - - nightly_binary_macos_conda_py3.7 - - binary_macos_conda: - filters: - branches: - only: nightly - name: nightly_binary_macos_conda_py3.8 - python_version: '3.8' - - binary_conda_upload: - context: org-member - filters: - branches: - only: nightly - name: nightly_binary_macos_conda_py3.8_upload - requires: - - nightly_binary_macos_conda_py3.8 - docker_build: - triggers: - - schedule: - cron: "0 10 * * 0" - filters: - branches: - only: - - master - jobs: - - docker_image_build \ No newline at end of file diff --git a/.circleci/config.yml.in b/.circleci/config.yml.in deleted file mode 100644 index fb3316969a..0000000000 --- a/.circleci/config.yml.in +++ /dev/null @@ -1,263 +0,0 @@ -version: 2.1 - -# How to test the Linux jobs: -# - Install CircleCI local CLI: https://circleci.com/docs/2.0/local-cli/ -# - circleci config process .circleci/config.yml > gen.yml && circleci local execute -c gen.yml --job binary_linux_wheel_py3.8 -# - Replace binary_linux_wheel_py3.8 with the name of the job you want to test. -# Job names are 'name:' key. - -orbs: - win: circleci/windows@2.0.0 - -binary_common: &binary_common - parameters: - # Edit these defaults to do a release - build_version: - description: "version number of release binary; by default, build a nightly" - type: string - default: "0.5.0" - pytorch_version: - description: "PyTorch version to build against; by default, use a nightly" - type: string - default: "1.5.0" - # Don't edit these - python_version: - description: "Python version to build against (e.g., 3.8)" - type: string - unicode_abi: - description: "Python 2.7 wheel only: whether or not we are cp27mu (default: no)" - type: string - default: "" - environment: - PYTHON_VERSION: << parameters.python_version >> - BUILD_VERSION: << parameters.build_version >> - PYTORCH_VERSION: << parameters.pytorch_version >> - UNICODE_ABI: << parameters.unicode_abi >> - CU_VERSION: cpu - -smoke_test_common: &smoke_test_common - <<: *binary_common - docker: - - image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/torchaudio/smoke_test:d26af7d0-8458-face-book-84239b5c75c0 - aws_auth: - aws_access_key_id: ${ECR_AWS_ACCESS_KEY} - aws_secret_access_key: ${ECR_AWS_SECRET_ACCESS_KEY} - -jobs: - circleci_consistency: - docker: - - image: circleci/python:3.8 - steps: - - checkout - - run: - command: | - pip install --user --progress-bar off jinja2 pyyaml - python .circleci/regenerate.py - git diff --exit-code || (echo ".circleci/config.yml not in sync with config.yml.in! Run .circleci/regenerate.py to update config"; exit 1) - - binary_linux_wheel: - <<: *binary_common - docker: - - image: "pytorch/manylinux-cuda100" - resource_class: 2xlarge+ - steps: - - checkout - - run: packaging/build_wheel.sh - - store_artifacts: - path: dist - - persist_to_workspace: - root: dist - paths: - - "*" - - binary_linux_conda: - <<: *binary_common - docker: - - image: "pytorch/conda-cuda" - resource_class: 2xlarge+ - steps: - - checkout - - run: packaging/build_conda.sh - - store_artifacts: - path: /opt/conda/conda-bld/linux-64 - - persist_to_workspace: - root: /opt/conda/conda-bld/linux-64 - paths: - - "*" - - binary_win_conda: - <<: *binary_common - executor: - name: win/default - shell: bash.exe - steps: - - checkout - - run: - command: | - choco install miniconda3 - (& "C:\tools\miniconda3\Scripts\conda.exe" "shell.powershell" "hook") | Out-String | Invoke-Expression - conda activate base - conda install -yq conda-build "conda-package-handling!=1.5.0" - bash packaging/build_conda.sh - shell: powershell.exe - - binary_macos_wheel: - <<: *binary_common - macos: - xcode: "9.0" - steps: - - checkout - - run: - # Cannot easily deduplicate this as source'ing activate - # will set environment variables which we need to propagate - # to build_wheel.sh - command: | - curl -o conda.sh https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh - sh conda.sh -b - source $HOME/miniconda3/bin/activate - packaging/build_wheel.sh - - store_artifacts: - path: dist - - persist_to_workspace: - root: dist - paths: - - "*" - - binary_macos_conda: - <<: *binary_common - macos: - xcode: "9.0" - steps: - - checkout - - run: - command: | - curl -o conda.sh https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh - sh conda.sh -b - source $HOME/miniconda3/bin/activate - conda install -yq conda-build - packaging/build_conda.sh - - store_artifacts: - path: /Users/distiller/miniconda3/conda-bld/osx-64 - - persist_to_workspace: - root: /Users/distiller/miniconda3/conda-bld/osx-64 - paths: - - "*" - - # Requires org-member context - binary_conda_upload: - docker: - - image: continuumio/miniconda - steps: - - attach_workspace: - at: ~/workspace - - run: - command: | - # Prevent credential from leaking - conda install -yq anaconda-client - set -x - anaconda -t "${CONDA_PYTORCHBOT_TOKEN}" upload ~/workspace/*.tar.bz2 -u pytorch-nightly --label main --no-progress --force - - # Requires org-member context - binary_wheel_upload: - docker: - - image: circleci/python:3.8 - steps: - - attach_workspace: - at: ~/workspace - - checkout - - run: - command: | - pip install --user awscli - export PATH="$HOME/.local/bin:$PATH" - # Prevent credential from leaking - set +x - export AWS_ACCESS_KEY_ID="${PYTORCH_BINARY_AWS_ACCESS_KEY_ID}" - export AWS_SECRET_ACCESS_KEY="${PYTORCH_BINARY_AWS_SECRET_ACCESS_KEY}" - set -x - for pkg in ~/workspace/*.whl; do - aws s3 cp "$pkg" "s3://pytorch/whl/nightly/" --acl public-read - done - - smoke_test_linux_conda: - <<: *smoke_test_common - steps: - - attach_workspace: - at: ~/workspace - - run: - name: install binaries - command: | - set -x - source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION} - conda install -v -y -c pytorch-nightly pytorch - conda install -v -y $(ls ~/workspace/torchaudio*.tar.bz2) - - run: - name: smoke test - command: | - source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION} - python -c "import torchaudio" - - smoke_test_linux_pip: - <<: *smoke_test_common - steps: - - attach_workspace: - at: ~/workspace - - run: - name: install binaries - command: | - set -x - source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION} - pip install $(ls ~/workspace/torchaudio*.whl) -f https://download.pytorch.org/whl/nightly/torch_nightly.html - - run: - name: smoke test - command: | - source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION} - python -c "import torchaudio" - - docker_image_build: - machine: - image: ubuntu-1604:201903-01 - resource_class: large - environment: - image_name: torchaudio/smoke_test - steps: - - checkout - - run: - name: build_docker image - no_output_timeout: "1h" - command: | - cd .circleci/docker && docker build . -t ${image_name}:${CIRCLE_WORKFLOW_ID} - - run: - name: upload docker image - no_output_timeout: "1h" - command: | - set +x - export AWS_ACCESS_KEY_ID=${ECR_AWS_ACCESS_KEY} - export AWS_SECRET_ACCESS_KEY=${ECR_AWS_SECRET_ACCESS_KEY} - eval $(aws ecr get-login --region us-east-1 --no-include-email) - set -x - docker tag ${image_name}:${CIRCLE_WORKFLOW_ID} 308535385114.dkr.ecr.us-east-1.amazonaws.com/${image_name}:${CIRCLE_WORKFLOW_ID} - docker push 308535385114.dkr.ecr.us-east-1.amazonaws.com/${image_name}:${CIRCLE_WORKFLOW_ID} - -workflows: - build: - jobs: - - circleci_consistency - {{ workflows() }} - - binary_win_conda: - name: torchaudio_win_py3.6 - python_version: "3.6" - - nightly: - jobs: - - circleci_consistency - {{ workflows(prefix="nightly_", filter_branch="nightly", upload=True) }} - docker_build: - triggers: - - schedule: - cron: "0 10 * * 0" - filters: - branches: - only: - - master - jobs: - - docker_image_build diff --git a/.circleci/docker/Dockerfile b/.circleci/docker/Dockerfile deleted file mode 100644 index 6066668694..0000000000 --- a/.circleci/docker/Dockerfile +++ /dev/null @@ -1,36 +0,0 @@ -# this Dockerfile is for torchaudio smoke test, it will be created periodically via CI system -# if you need to do it locally, follow below steps once you have Docker installed -# assuming you're within the directory where this Dockerfile located -# $ docker build . -t torchaudio/smoketest - -# if you want to push to aws ecr, make sure you have the rights to write to ECR, then run -# $ eval $(aws ecr get-login --region us-east-1 --no-include-email) -# $ export MYTAG=localbuild ## you can choose whatever tag you like -# $ docker tag torchaudio/smoketest 308535385114.dkr.ecr.us-east-1.amazonaws.com/torchaudio/smoke_test:${MYTAG} -# $ docker push 308535385114.dkr.ecr.us-east-1.amazonaws.com/torchaudio/smoke_test:${MYTAG} - -FROM ubuntu:latest - -RUN apt-get -qq update && apt-get -qq -y install curl bzip2 sox libsox-dev libsox-fmt-all \ - && curl -sSL https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -o /tmp/miniconda.sh \ - && bash /tmp/miniconda.sh -bfp /usr/local \ - && rm -rf /tmp/miniconda.sh \ - && conda install -y python=3 \ - && conda update conda \ - && apt-get -qq -y remove curl bzip2 \ - && apt-get -qq -y autoremove \ - && apt-get autoclean \ - && rm -rf /var/lib/apt/lists/* /var/log/dpkg.log \ - && conda clean --all --yes - -ENV PATH /opt/conda/bin:$PATH - -RUN conda create -y --name python3.5 python=3.5 -RUN conda create -y --name python3.6 python=3.6 -RUN conda create -y --name python3.7 python=3.7 -SHELL [ "/bin/bash", "-c" ] -RUN echo "source /usr/local/etc/profile.d/conda.sh" >> ~/.bashrc -RUN source /usr/local/etc/profile.d/conda.sh && conda activate python3.5 && conda install -y -c conda-forge sox && conda install -y numpy -RUN source /usr/local/etc/profile.d/conda.sh && conda activate python3.6 && conda install -y -c conda-forge sox && conda install -y numpy -RUN source /usr/local/etc/profile.d/conda.sh && conda activate python3.7 && conda install -y -c conda-forge sox && conda install -y numpy -CMD [ "/bin/bash"] diff --git a/.circleci/regenerate.py b/.circleci/regenerate.py deleted file mode 100755 index 19f52be756..0000000000 --- a/.circleci/regenerate.py +++ /dev/null @@ -1,124 +0,0 @@ -#!/usr/bin/env python3 - -""" -This script should use a very simple, functional programming style. -Avoid Jinja macros in favor of native Python functions. - -Don't go overboard on code generation; use Python only to generate -content that can't be easily declared statically using CircleCI's YAML API. - -Data declarations (e.g. the nested loops for defining the configuration matrix) -should be at the top of the file for easy updating. - -See this comment for design rationale: -https://github.com/pytorch/vision/pull/1321#issuecomment-531033978 -""" - -import jinja2 -import yaml -import os.path - - -def workflows(prefix='', upload=False, filter_branch=None, indentation=6): - w = [] - for btype in ["wheel", "conda"]: - for os_type in ["linux", "macos"]: - for python_version in ["3.5", "3.6", "3.7", "3.8"]: - for unicode in ([False, True] if btype == "wheel" and python_version == "2.7" else [False]): - w += workflow_pair(btype, os_type, python_version, unicode, filter_branch, prefix, upload) - - return indent(indentation, w) - - -def workflow_pair(btype, os_type, python_version, unicode, filter_branch, prefix='', upload=False): - - w = [] - unicode_suffix = "_unicode" if unicode else "" - base_workflow_name = "{prefix}binary_{os_type}_{btype}_py{python_version}{unicode_suffix}".format( - prefix=prefix, - os_type=os_type, - btype=btype, - python_version=python_version, - unicode_suffix=unicode_suffix) - - w.append(generate_base_workflow(base_workflow_name, python_version, unicode, filter_branch, os_type, btype)) - - if upload: - - is_py3_linux = os_type == 'linux' and not python_version.startswith("2.") - - w.append(generate_upload_workflow(base_workflow_name, filter_branch, btype)) - - if filter_branch == 'nightly' and is_py3_linux: - pydistro = 'pip' if btype == 'wheel' else 'conda' - w.append(generate_smoketest_workflow(pydistro, base_workflow_name, filter_branch, python_version)) - - return w - - -def generate_base_workflow(base_workflow_name, python_version, unicode, filter_branch, os_type, btype): - - d = { - "name": base_workflow_name, - "python_version": python_version, - } - - if unicode: - d["unicode_abi"] = '1' - - if filter_branch: - d["filters"] = gen_filter_branch_tree(filter_branch) - - return {"binary_{os_type}_{btype}".format(os_type=os_type, btype=btype): d} - - -def gen_filter_branch_tree(branch_name): - return {"branches": {"only": branch_name}} - - -def generate_upload_workflow(base_workflow_name, filter_branch, btype): - d = { - "name": "{base_workflow_name}_upload".format(base_workflow_name=base_workflow_name), - "context": "org-member", - "requires": [base_workflow_name], - } - - if filter_branch: - d["filters"] = gen_filter_branch_tree(filter_branch) - - return {"binary_{btype}_upload".format(btype=btype): d} - - -def generate_smoketest_workflow(pydistro, base_workflow_name, filter_branch, python_version): - - required_build_suffix = "_upload" - required_build_name = base_workflow_name + required_build_suffix - - smoke_suffix = "smoke_test_{pydistro}".format(pydistro=pydistro) - d = { - "name": "{base_workflow_name}_{smoke_suffix}".format( - base_workflow_name=base_workflow_name, smoke_suffix=smoke_suffix), - "requires": [required_build_name], - "python_version": python_version, - } - - if filter_branch: - d["filters"] = gen_filter_branch_tree(filter_branch) - - return {"smoke_test_linux_{pydistro}".format(pydistro=pydistro): d} - - -def indent(indentation, data_list): - return ("\n" + " " * indentation).join(yaml.dump(data_list).splitlines()) - - -if __name__ == "__main__": - d = os.path.dirname(__file__) - env = jinja2.Environment( - loader=jinja2.FileSystemLoader(d), - lstrip_blocks=True, - autoescape=False, - ) - - with open(os.path.join(d, 'config.yml'), 'w') as f: - f.write(env.get_template('config.yml.in').render(workflows=workflows)) diff --git a/.circleci/test/test_sort_yaml.py b/.circleci/test/test_sort_yaml.py deleted file mode 100755 index dc6db481dd..0000000000 --- a/.circleci/test/test_sort_yaml.py +++ /dev/null @@ -1,14 +0,0 @@ -#!/usr/bin/env python3 - -""" -To compare new version with previous: - - ./regenerate.sh - meld <(git show HEAD:./config.yml | ./sort-yaml.py) <(cat config.yml | ./sort-yaml.py) -""" - - -import sys -import yaml - -sys.stdout.write(yaml.dump(yaml.load(sys.stdin, Loader=yaml.FullLoader), sort_keys=True)) diff --git a/.clang-format b/.clang-format deleted file mode 100644 index 73304266bd..0000000000 --- a/.clang-format +++ /dev/null @@ -1,88 +0,0 @@ ---- -AccessModifierOffset: -1 -AlignAfterOpenBracket: AlwaysBreak -AlignConsecutiveAssignments: false -AlignConsecutiveDeclarations: false -AlignEscapedNewlinesLeft: true -AlignOperands: false -AlignTrailingComments: false -AllowAllParametersOfDeclarationOnNextLine: false -AllowShortBlocksOnASingleLine: false -AllowShortCaseLabelsOnASingleLine: false -AllowShortFunctionsOnASingleLine: Empty -AllowShortIfStatementsOnASingleLine: false -AllowShortLoopsOnASingleLine: false -AlwaysBreakAfterReturnType: None -AlwaysBreakBeforeMultilineStrings: true -AlwaysBreakTemplateDeclarations: true -BinPackArguments: false -BinPackParameters: false -BraceWrapping: - AfterClass: false - AfterControlStatement: false - AfterEnum: false - AfterFunction: false - AfterNamespace: false - AfterObjCDeclaration: false - AfterStruct: false - AfterUnion: false - BeforeCatch: false - BeforeElse: false - IndentBraces: false -BreakBeforeBinaryOperators: None -BreakBeforeBraces: Attach -BreakBeforeTernaryOperators: true -BreakConstructorInitializersBeforeComma: false -BreakAfterJavaFieldAnnotations: false -BreakStringLiterals: false -ColumnLimit: 80 -CommentPragmas: '^ IWYU pragma:' -CompactNamespaces: false -ConstructorInitializerAllOnOneLineOrOnePerLine: true -ConstructorInitializerIndentWidth: 4 -ContinuationIndentWidth: 4 -Cpp11BracedListStyle: true -DerivePointerAlignment: false -DisableFormat: false -ForEachMacros: [ FOR_EACH_RANGE, FOR_EACH, ] -IncludeCategories: - - Regex: '^<.*\.h(pp)?>' - Priority: 1 - - Regex: '^<.*' - Priority: 2 - - Regex: '.*' - Priority: 3 -IndentCaseLabels: true -IndentWidth: 2 -IndentWrappedFunctionNames: false -KeepEmptyLinesAtTheStartOfBlocks: false -MacroBlockBegin: '' -MacroBlockEnd: '' -MaxEmptyLinesToKeep: 1 -NamespaceIndentation: None -ObjCBlockIndentWidth: 2 -ObjCSpaceAfterProperty: false -ObjCSpaceBeforeProtocolList: false -PenaltyBreakBeforeFirstCallParameter: 1 -PenaltyBreakComment: 300 -PenaltyBreakFirstLessLess: 120 -PenaltyBreakString: 1000 -PenaltyExcessCharacter: 1000000 -PenaltyReturnTypeOnItsOwnLine: 2000000 -PointerAlignment: Left -ReflowComments: true -SortIncludes: true -SpaceAfterCStyleCast: false -SpaceBeforeAssignmentOperators: true -SpaceBeforeParens: ControlStatements -SpaceInEmptyParentheses: false -SpacesBeforeTrailingComments: 1 -SpacesInAngles: false -SpacesInContainerLiterals: true -SpacesInCStyleCastParentheses: false -SpacesInParentheses: false -SpacesInSquareBrackets: false -Standard: Cpp11 -TabWidth: 8 -UseTab: Never -... diff --git a/.clang-tidy b/.clang-tidy deleted file mode 100644 index a3a40b5907..0000000000 --- a/.clang-tidy +++ /dev/null @@ -1,33 +0,0 @@ ---- -# NOTE there must be no spaces before the '-', so put the comma first. -Checks: ' - -* - ,bugprone-* - ,-bugprone-forward-declaration-namespace - ,-bugprone-macro-parentheses - ,cppcoreguidelines-* - ,-cppcoreguidelines-interfaces-global-init - ,-cppcoreguidelines-owning-memory - ,-cppcoreguidelines-pro-bounds-array-to-pointer-decay - ,-cppcoreguidelines-pro-bounds-constant-array-index - ,-cppcoreguidelines-pro-bounds-pointer-arithmetic - ,-cppcoreguidelines-pro-type-cstyle-cast - ,-cppcoreguidelines-pro-type-reinterpret-cast - ,-cppcoreguidelines-pro-type-static-cast-downcast - ,-cppcoreguidelines-pro-type-union-access - ,-cppcoreguidelines-pro-type-vararg - ,-cppcoreguidelines-special-member-functions - ,hicpp-exception-baseclass - ,hicpp-avoid-goto - ,modernize-* - ,-modernize-return-braced-init-list - ,-modernize-use-auto - ,-modernize-use-default-member-init - ,-modernize-use-using - ,performance-unnecessary-value-param - ' -WarningsAsErrors: '*' -HeaderFilterRegex: 'torchaudio/.*' -AnalyzeTemporaryDtors: false -CheckOptions: -... diff --git a/.flake8 b/.flake8 deleted file mode 100644 index 388d81d723..0000000000 --- a/.flake8 +++ /dev/null @@ -1,4 +0,0 @@ -[flake8] -max-line-length = 120 -ignore = E305,E402,E721,E741,F401,F403,F405,F999,W503,W504 -exclude = build,docs/source,_ext diff --git a/.github/ISSUE_TEMPLATE/bug-report.md b/.github/ISSUE_TEMPLATE/bug-report.md deleted file mode 100644 index fb50bd8504..0000000000 --- a/.github/ISSUE_TEMPLATE/bug-report.md +++ /dev/null @@ -1,53 +0,0 @@ ---- -name: "\U0001F41B Bug Report" -about: Submit a bug report to help us improve Torchaudio - ---- - -## ๐Ÿ› Bug - - - -## To Reproduce - -Steps to reproduce the behavior: - -1. -1. -1. - - - -## Expected behavior - - - -## Environment - - - What commands did you used to install torchaudio (conda/pip/build from source)? - - If you are building from source, which commit is it? - - What does `torchaudio.__version__` print? (If applicable) - -Please copy and paste the output from our -[environment collection script](https://raw.githubusercontent.com/pytorch/pytorch/master/torch/utils/collect_env.py) -(or fill out the checklist below manually). - -You can get the script and run it with: -``` -wget https://raw.githubusercontent.com/pytorch/pytorch/master/torch/utils/collect_env.py -# For security purposes, please check the contents of collect_env.py before running it. -python collect_env.py -``` - - - PyTorch Version (e.g., 1.0): - - OS (e.g., Linux): - - How you installed PyTorch (`conda`, `pip`, source): - - Build command you used (if compiling from source): - - Python version: - - CUDA/cuDNN version: - - GPU models and configuration: - - Any other relevant information: - -## Additional context - - diff --git a/.github/ISSUE_TEMPLATE/documentation.md b/.github/ISSUE_TEMPLATE/documentation.md deleted file mode 100644 index ae745121c5..0000000000 --- a/.github/ISSUE_TEMPLATE/documentation.md +++ /dev/null @@ -1,9 +0,0 @@ ---- -name: "\U0001F4DA Documentation" -about: Report an issue related to https://pytorch.org/audio - ---- - -## ๐Ÿ“š Documentation - - diff --git a/.github/ISSUE_TEMPLATE/feature-request.md b/.github/ISSUE_TEMPLATE/feature-request.md deleted file mode 100644 index f3896e5368..0000000000 --- a/.github/ISSUE_TEMPLATE/feature-request.md +++ /dev/null @@ -1,24 +0,0 @@ ---- -name: "\U0001F680Feature Request" -about: Submit a proposal/request for a new Torchaudio feature - ---- - -## ๐Ÿš€ Feature - - -## Motivation - - - -## Pitch - - - -## Alternatives - - - -## Additional context - - diff --git a/.github/ISSUE_TEMPLATE/questions-help-support.md b/.github/ISSUE_TEMPLATE/questions-help-support.md deleted file mode 100644 index 77bfb55b9a..0000000000 --- a/.github/ISSUE_TEMPLATE/questions-help-support.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -name: "โ“Questions/Help/Support" -about: Do you need support? We have resources. - ---- - -## โ“ Questions and Help - -### Please note that this issue tracker is not a help form and this issue will be closed. - -We have a set of [listed resources available on the website](https://pytorch.org/resources). Our primary means of support is our discussion forum: - -- [Discussion Forum](https://discuss.pytorch.org/) diff --git a/.gitignore b/.gitignore deleted file mode 100644 index 41a434acb2..0000000000 --- a/.gitignore +++ /dev/null @@ -1,123 +0,0 @@ -# Byte-compiled / optimized / DLL files -__pycache__/ -*.py[cod] -*$py.class - -# temp files -~* -*.swp - -# C extensions / folders -*.so -_ext/ - -# Distribution / packaging -.Python -env/ -build/ -develop-eggs/ -dist/ -downloads/ -eggs/ -.eggs/ -lib/ -lib64/ -parts/ -sdist/ -var/ -wheels/ -*.egg-info/ -.installed.cfg -*.egg - -# PyInstaller -# Usually these files are written by a python script from a template -# before PyInstaller builds the exe, so as to inject date/other infos into it. -*.manifest -*.spec - -# Installer logs -pip-log.txt -pip-delete-this-directory.txt - -# Unit test / coverage reports -htmlcov/ -.tox/ -.coverage -.coverage.* -.cache -nosetests.xml -coverage.xml -*.cover -.hypothesis/ - -# Translations -*.mo -*.pot - -# Django stuff: -*.log -local_settings.py - -# Flask stuff: -instance/ -.webassets-cache - -# Scrapy stuff: -.scrapy - -# Sphinx documentation -docs/_build/ -docs/src/ - -# PyBuilder -target/ - -# Jupyter Notebook -.ipynb_checkpoints/ - -# pyenv -.python-version - -# celery beat schedule file -celerybeat-schedule - -# SageMath parsed files -*.sage.py - -# dotenv -.env - -# virtualenv -.venv -venv/ -ENV/ - -# Spyder project settings -.spyderproject -.spyproject - -# Rope project settings -.ropeproject - -# PyCharm project settings -.idea - -# OSX dir files -.DS_Store - -# mkdocs documentation -/site - -# mypy -.mypy_cache/ - -# Generated Files -test/assets/sinewave.wav -torchaudio/version.py -gen.yml - -# Examples -examples/interactive_asr/data/*.txt -examples/interactive_asr/data/*.model -examples/interactive_asr/data/*.pt diff --git a/.python3 b/.python3 new file mode 100644 index 0000000000..e69de29bb2 diff --git a/.travis.yml b/.travis.yml deleted file mode 100644 index 1fd00c3e96..0000000000 --- a/.travis.yml +++ /dev/null @@ -1,36 +0,0 @@ -# note dist: 'trusty' does not work here -dist: xenial - -language: python - -# cache miniconda installer and similar files -cache: - directories: - - /home/travis/download - -# This matrix tests that the code works on Python 3.5, 3.6, 3.7, passes -# lint and example tests. -matrix: - fast_finish: true - include: - - env: PYTHON_VERSION="3.5" - - env: PYTHON_VERSION="3.6" - - env: PYTHON_VERSION="3.7" - - env: PYTHON_VERSION="3.5" RUN_FLAKE8="true" SKIP_INSTALL="true" SKIP_TESTS="true" - - env: PYTHON_VERSION="3.5" RUN_EXAMPLE_TESTS="true" SKIP_TESTS="true" - allow_failures: - - env: PYTHON_VERSION="3.5" RUN_EXAMPLE_TESTS="true" SKIP_TESTS="true" - -addons: - apt: - packages: - sox - libsox-dev - libsox-fmt-all - portaudio19-dev - -notifications: - email: false - -install: source build_tools/travis/install.sh -script: bash build_tools/travis/test_script.sh From e2430778611a4ea26a1913e327525901953c5d11 Mon Sep 17 00:00:00 2001 From: Christian Puhrsch Date: Wed, 29 Apr 2020 00:29:57 -0400 Subject: [PATCH 03/19] Set up ShipIt fbshipit-source-id: 4fb853c391900d3070b936e5a3e4609eb78a780d From 0d99b9d0a4e8d994ddd881feebff4857e3206efd Mon Sep 17 00:00:00 2001 From: Christian Puhrsch Date: Tue, 28 Apr 2020 19:16:39 -0700 Subject: [PATCH 04/19] 20200428 pytorch/audio import Summary: [10:30:47: cpuhrsch@devvm3140 pytorch]$ ./fb_build/import_audio.sh Reviewed By: vincentqb Differential Revision: D21282421 fbshipit-source-id: 9bde1455ca6a19defbf33dbbfc5f0d49a8e4dc6a --- CODE_OF_CONDUCT.md | 76 ++ README.md | 2 +- build_tools/travis/install.sh | 1 + build_tools/travis/test_script.sh | 1 + docs/source/datasets.rst | 36 +- docs/source/functional.rst | 99 ++- docs/source/transforms.rst | 16 +- mypy.ini | 3 + packaging/build_conda.sh | 2 +- packaging/build_wheel.sh | 2 +- requirements.txt | 5 +- setup.py | 2 +- test/assets/vad-hello-mono-32000.wav | Bin 0 -> 275106 bytes test/assets/vad-hello-stereo-44100.wav | Bin 0 -> 937862 bytes test/common_utils.py | 23 +- test/test_batch_consistency.py | 156 ++-- test/test_compliance_kaldi.py | 25 +- test/test_dataloader.py | 29 +- test/test_datasets.py | 4 +- test/test_functional.py | 70 +- test/{test.py => test_io.py} | 0 test/test_kaldi_io.py | 8 +- test/test_librosa_compatibility.py | 43 +- ...filtering.py => test_sox_compatibility.py} | 321 ++++----- test/test_sox_effects.py | 36 +- test/test_torchscript_consistency.py | 671 +++++++++++------- test/test_transforms.py | 25 +- torchaudio/__init__.py | 184 +++-- torchaudio/_backend.py | 22 +- torchaudio/_soundfile_backend.py | 52 +- torchaudio/_sox_backend.py | 32 +- torchaudio/common_utils.py | 2 +- torchaudio/datasets/commonvoice.py | 96 ++- torchaudio/datasets/librispeech.py | 44 +- torchaudio/datasets/ljspeech.py | 23 +- torchaudio/datasets/speechcommands.py | 29 +- torchaudio/datasets/utils.py | 105 +-- torchaudio/datasets/vctk.py | 53 +- torchaudio/datasets/yesno.py | 37 +- torchaudio/functional.py | 582 +++++++++++++-- torchaudio/kaldi_io.py | 38 +- torchaudio/sox_effects.py | 9 +- torchaudio/transforms.py | 166 ++++- 43 files changed, 2145 insertions(+), 985 deletions(-) create mode 100644 CODE_OF_CONDUCT.md create mode 100644 mypy.ini create mode 100644 test/assets/vad-hello-mono-32000.wav create mode 100644 test/assets/vad-hello-stereo-44100.wav rename test/{test.py => test_io.py} (100%) rename test/{test_functional_filtering.py => test_sox_compatibility.py} (54%) diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md new file mode 100644 index 0000000000..b91e23b17c --- /dev/null +++ b/CODE_OF_CONDUCT.md @@ -0,0 +1,76 @@ +# Code of Conduct + +## Our Pledge + +In the interest of fostering an open and welcoming environment, we as +contributors and maintainers pledge to make participation in our project and +our community a harassment-free experience for everyone, regardless of age, body +size, disability, ethnicity, sex characteristics, gender identity and expression, +level of experience, education, socio-economic status, nationality, personal +appearance, race, religion, or sexual identity and orientation. + +## Our Standards + +Examples of behavior that contributes to creating a positive environment +include: + +* Using welcoming and inclusive language +* Being respectful of differing viewpoints and experiences +* Gracefully accepting constructive criticism +* Focusing on what is best for the community +* Showing empathy towards other community members + +Examples of unacceptable behavior by participants include: + +* The use of sexualized language or imagery and unwelcome sexual attention or +advances +* Trolling, insulting/derogatory comments, and personal or political attacks +* Public or private harassment +* Publishing others' private information, such as a physical or electronic +address, without explicit permission +* Other conduct which could reasonably be considered inappropriate in a +professional setting + +## Our Responsibilities + +Project maintainers are responsible for clarifying the standards of acceptable +behavior and are expected to take appropriate and fair corrective action in +response to any instances of unacceptable behavior. + +Project maintainers have the right and responsibility to remove, edit, or +reject comments, commits, code, wiki edits, issues, and other contributions +that are not aligned to this Code of Conduct, or to ban temporarily or +permanently any contributor for other behaviors that they deem inappropriate, +threatening, offensive, or harmful. + +## Scope + +This Code of Conduct applies within all project spaces, and it also applies when +an individual is representing the project or its community in public spaces. +Examples of representing a project or community include using an official +project e-mail address, posting via an official social media account, or acting +as an appointed representative at an online or offline event. Representation of +a project may be further defined and clarified by project maintainers. + +## Enforcement + +Instances of abusive, harassing, or otherwise unacceptable behavior may be +reported by contacting the project team at . All +complaints will be reviewed and investigated and will result in a response that +is deemed necessary and appropriate to the circumstances. The project team is +obligated to maintain confidentiality with regard to the reporter of an incident. +Further details of specific enforcement policies may be posted separately. + +Project maintainers who do not follow or enforce the Code of Conduct in good +faith may face temporary or permanent repercussions as determined by other +members of the project's leadership. + +## Attribution + +This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, +available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html + +[homepage]: https://www.contributor-covenant.org + +For answers to common questions about this code of conduct, see +https://www.contributor-covenant.org/faq diff --git a/README.md b/README.md index f36f19db36..816fd75f6c 100644 --- a/README.md +++ b/README.md @@ -70,7 +70,7 @@ build nightlies based on PyTorch nightlies by hand following the instructions in ``` pip install numpy -pip install torchaudio_nightly -f https://download.pytorch.org/whl/nightly/torch_nightly.html +pip install --pre torchaudio -f https://download.pytorch.org/whl/nightly/torch_nightly.html ``` To install the latest nightly via conda, run: diff --git a/build_tools/travis/install.sh b/build_tools/travis/install.sh index 61c564a5bf..3586d5bd08 100644 --- a/build_tools/travis/install.sh +++ b/build_tools/travis/install.sh @@ -48,6 +48,7 @@ popd source activate testenv # Install requirements via pip in our conda environment +conda install -y pytorch cpuonly -c pytorch-nightly pip install -r requirements.txt # Install the following only if running tests diff --git a/build_tools/travis/test_script.sh b/build_tools/travis/test_script.sh index 06d70956b8..e854a211b3 100755 --- a/build_tools/travis/test_script.sh +++ b/build_tools/travis/test_script.sh @@ -6,6 +6,7 @@ set -e python --version +python -c 'import torch;print("torch:", torch.__version__)' run_tests() { # find all the test files that match "test*.py" diff --git a/docs/source/datasets.rst b/docs/source/datasets.rst index eef25da616..27d4b20a34 100644 --- a/docs/source/datasets.rst +++ b/docs/source/datasets.rst @@ -25,15 +25,47 @@ All the datasets have almost similar API. They all have two common arguments: .. currentmodule:: torchaudio.datasets +COMMONVOICE +~~~~~~~~~~~ + +.. autoclass:: COMMONVOICE + :members: __getitem__ + :special-members: + + +LIBRISPEECH +~~~~~~~~~~~ + +.. autoclass:: LIBRISPEECH + :members: __getitem__ + :special-members: + + +LJSPEECH +~~~~~~~~ + +.. autoclass:: LJSPEECH + :members: __getitem__ + :special-members: + + +SPEECHCOMMANDS +~~~~~~~~~~~~~~ + +.. autoclass:: SPEECHCOMMANDS + :members: __getitem__ + :special-members: + + VCTK -~~~~~ +~~~~ .. autoclass:: VCTK :members: __getitem__ :special-members: YESNO -~~~~~~~~~~~~~ +~~~~~ .. autoclass:: YESNO :members: __getitem__ diff --git a/docs/source/functional.rst b/docs/source/functional.rst index a76bf4dc4c..d8f318ea18 100644 --- a/docs/source/functional.rst +++ b/docs/source/functional.rst @@ -2,113 +2,168 @@ :class: hidden-section torchaudio.functional -====================== +===================== .. currentmodule:: torchaudio.functional Functions to perform common audio operations. :hidden:`istft` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~ .. autofunction:: istft :hidden:`spectrogram` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~ .. autofunction:: spectrogram :hidden:`amplitude_to_DB` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~~~~~ .. autofunction:: amplitude_to_DB :hidden:`create_fb_matrix` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~~~~~~ .. autofunction:: create_fb_matrix :hidden:`create_dct` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~ .. autofunction:: create_dct :hidden:`mu_law_encoding` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~~~~~ .. autofunction:: mu_law_encoding :hidden:`mu_law_decoding` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~~~~~ .. autofunction:: mu_law_decoding :hidden:`complex_norm` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~~ .. autofunction:: complex_norm :hidden:`angle` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~ .. autofunction:: angle :hidden:`magphase` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~ .. autofunction:: magphase :hidden:`phase_vocoder` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~~~ .. autofunction:: phase_vocoder :hidden:`lfilter` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~ .. autofunction:: lfilter :hidden:`biquad` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~ .. autofunction:: biquad :hidden:`lowpass_biquad` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~~~~ .. autofunction:: lowpass_biquad :hidden:`highpass_biquad` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~~~~~ .. autofunction:: highpass_biquad :hidden:`allpass_biquad` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~~~~ .. autofunction:: allpass_biquad :hidden:`equalizer_biquad` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~~~~~~ .. autofunction:: equalizer_biquad -:hidden:`mask_along_axis` +:hidden:`bandpass_biquad` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autofunction:: bandpass_biquad + +:hidden:`bandreject_biquad` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autofunction:: bandreject_biquad + +:hidden:`band_biquad` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autofunction:: band_biquad + +:hidden:`treble_biquad` ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +.. autofunction:: treble_biquad + +:hidden:`deemph_biquad` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autofunction:: deemph_biquad + +:hidden:`riaa_biquad` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autofunction:: riaa_biquad + +:hidden:`contrast` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autofunction:: contrast + +:hidden:`dcshift` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autofunction:: dcshift + +:hidden:`overdrive` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autofunction:: overdrive + +:hidden:`mask_along_axis` +~~~~~~~~~~~~~~~~~~~~~~~~~ + .. autofunction:: mask_along_axis :hidden:`mask_along_axis_iid` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. autofunction:: mask_along_axis_iid :hidden:`compute_deltas` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~~~~ .. autofunction:: compute_deltas :hidden:`detect_pitch_frequency` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. autofunction:: detect_pitch_frequency + +:hidden:`sliding_window_cmn` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autofunction:: sliding_window_cmn + +:hidden:`vad` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autofunction:: vad diff --git a/docs/source/transforms.rst b/docs/source/transforms.rst index aa50b33714..fc6a4d4a9f 100644 --- a/docs/source/transforms.rst +++ b/docs/source/transforms.rst @@ -19,7 +19,7 @@ Transforms are common audio transforms. They can be chained together using :clas :hidden:`GriffinLim` ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -.. autoclass:: GriffinLim +.. autoclass:: GriffinLim .. automethod:: forward @@ -128,3 +128,17 @@ Transforms are common audio transforms. They can be chained together using :clas .. autoclass:: Vol .. automethod:: forward + +:hidden:`SlidingWindowCmn` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: SlidingWindowCmn + + .. automethod:: forward + +:hidden:`Vad` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: Vad + + .. automethod:: forward diff --git a/mypy.ini b/mypy.ini new file mode 100644 index 0000000000..068f0918a3 --- /dev/null +++ b/mypy.ini @@ -0,0 +1,3 @@ +[mypy] +allow_redefinition = True +ignore_missing_imports = True diff --git a/packaging/build_conda.sh b/packaging/build_conda.sh index b75bbf8a13..3e011d312f 100755 --- a/packaging/build_conda.sh +++ b/packaging/build_conda.sh @@ -6,7 +6,7 @@ script_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" export BUILD_TYPE="conda" export NO_CUDA_PACKAGE=1 -setup_env 0.5.0 +setup_env 0.6.0 export SOURCE_ROOT_DIR="$PWD" setup_conda_pytorch_constraint conda build $CONDA_CHANNEL_FLAGS --no-anaconda-upload --python "$PYTHON_VERSION" packaging/torchaudio diff --git a/packaging/build_wheel.sh b/packaging/build_wheel.sh index 1e4f927ef5..fc0742ece1 100755 --- a/packaging/build_wheel.sh +++ b/packaging/build_wheel.sh @@ -6,7 +6,7 @@ script_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" export BUILD_TYPE="wheel" export NO_CUDA_PACKAGE=1 -setup_env 0.5.0 +setup_env 0.6.0 setup_wheel_python "$script_dir/build_from_source.sh" "$(pwd)" # Build static dependencies pip_install numpy future diff --git a/requirements.txt b/requirements.txt index c67dd4627a..316bd5c7fd 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ -torch>=1.2.0 +torch>=1.4.0 # Required for Windows because it's the only available backend SoundFile; sys_platform == 'win32' @@ -18,6 +18,3 @@ scipy # Unit tests with pytest pytest - -# Testing only Py3 compat -backports.tempfile diff --git a/setup.py b/setup.py index 8382c1114c..0128a2fe61 100644 --- a/setup.py +++ b/setup.py @@ -55,7 +55,7 @@ def check_env_flag(name, default=''): # Creating the version file cwd = os.path.dirname(os.path.abspath(__file__)) -version = '0.5.0a0' +version = '0.6.0a0' sha = 'Unknown' try: diff --git a/test/assets/vad-hello-mono-32000.wav b/test/assets/vad-hello-mono-32000.wav new file mode 100644 index 0000000000000000000000000000000000000000..1822c3196c5e1b744d07c20bfe65035b76c4db7a GIT binary patch literal 275106 zcmZU+2b2`W_x{~sb~duWl3~fPX zBuLJZb52XxWz$TD_w#grIsX3VrO)il^mJEO-SFH{RhQekbnG~#dBhlS)9oD|82)s% z5{6-z9L5WLh#7`ulrV-3d3wlbuF)^yKhOW~b9hGggonOgz0NTF;GgGvyz3SI{qOtL zyZ?LFy}Ih^U;X0yCa>@sebXzv#xm^x-*c}%UGM6XuDyD{?!EfE&KpM9m$2>d+5h)R zy7T}3Q+xXN|Ni~&s;e#NI*m8{9`;u~4r!n#|KFZmeZqfFzItEyoR9+HRaehkegE&b zt7E)6qVNj+RlEPS{a-ubxsXO0)79_)_51%F>D6bc_g9~P`kDeh8hu{6+I`t~Tk-*b+=-mpg3hxeEQBOkIy4pXrrGAI)#r^pIo*IrIq~8B+ zLH$twis)fMa1?ST?4L_dbw}8fDB}u8o5UECgQsY`HnfQ`%FF&m##=-i`gFlR=b!SA z`p5h;e7?dJYQ+!680GF%G&aLX5AJsYN)__72)`6Fit|dMT{md0D4$O~ zRdUc?3_9oV+<4GapZ7wpy69*7XZ_Ruae8ow<2b#^;VDjVZz`=vcyg+d#aDVBBZiW^^<<7`Ga2Id0)-!?y>G=Zz=%{Z6Bw(bc%o zXlXR&nbnMnMy65Ks6=myL7Orh#i2$LuaL%ZMtO;mdTfsZK`P_WSGnt&CMP zKgn^1>vG^p0axpukQ-+LO65YEbDX&ZbuI3_0_7?jEsTan1;$j~C<~WQGsfNiVJLW- z7LWS}`FxIc&T(9#T}iWK!=sJ7fa}ti1bUkm^u9DLrSmyGXt6Zs(u|UfO5@QL(iq7= z6zP%#s+AmCzT}^WZ>NG$h<8VLouaQf!8k7PS9}(irA^X7X`ot?L?+M&JNTXw@K(=} z#HR2})5vBF$DoZ`7nP1NuG2hEv^m53rJx6KzXUueOCLpfjUWS#6z5DGqd7FH&aai> zQ%NJv^&uxLMp@IS!W~y=EuZ5A^xFkj_VITwBQoeogf?p!jRIa*;|_=O8c{YJNTmI; zJS9EQiV}Rv;Co3%kr>=*)03)31D;+cxI)rVicgZ{#9;iQa526|p@A?#qn5r(mTcn; z96ZhQcKhp*ll@TjFn!y@>(KvbavWNph6We7>WZI2|5Cvai?&6}kP0Csia~*EjO&rw zDoAW~qZT8|LUwGU06q#cT%^*2Ho3GUYUsQqwutZIWIp^8a>au)!Uo}~%e#;#&R-+o zYc$}d_?r@pBQ2mt8sBaDRL!UhO)47ojaG1`9vr9!XR2_Npe-RqA~f;f^lnCc8tD}# zNScoXa&?x`N;}TO{WHiwcF?o)@N1L50}5T>%o*-D%WtOxzMl8D`&;-Fp;bwXv_R4? zeF(>LiE)*OA{FUhDqJiD7b^yORWgvH4EQEai*w?RBr}Qkkjv5y%eaX2oDbw%6qNQ# ze=mTlm*`P8&&}cUWu7Z~iDDtuq!A`M5K4e#poo4SrvJjdJX*QL-vTHT@}n@|jmLG; zLGdFGS=h_S^$bxe&rhenX@MLFl}jK8rI3sg^trfE+i1c_%NykwUs=wU4m3Q|XbPg$ z=WI#tEzk9m=~9d|hjE>y%|iMvxfj5wsSN+Qievd(aAwG=VPote0fmDg|Hlw)0^K+rZ zI)9nJk@svyb26Z=#h8*9hwS|o`hNf#UEsGPymDz(s1<{+(v<4ZsV$Uhi50rpxFOIZ zAzW#mC>i$yYbhD1NY9he@5}TwgPu#TB%RWoZ2DOgX!0qfXD3|Q1wLh;IPILL4@o>B zN)IhYC=W>7l9y)lDLojqq@*%@$Yk8|0VKEbdZIit1Knx~+Lb}lrHvJli-sIE;7T#B z5MP80!tN+kN<)4uXe$KIfEsc7b%LHq!_RV^ZIfxV;!ZvK9zXtcX^jY$!Hj>d! 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""" tmp_dir = tempfile.TemporaryDirectory() - copytree(os.path.join(TEST_DIR_PATH, "assets"), + copytree(os.path.join(_TEST_DIR_PATH, "assets"), os.path.join(tmp_dir.name, "assets")) return tmp_dir.name, tmp_dir @@ -47,12 +52,6 @@ def random_float_tensor(seed, size, a=22695477, c=1, m=2 ** 32): return torch.tensor(arr).float().view(size) / m -def random_int_tensor(seed, size, low=0, high=2 ** 32, a=22695477, c=1, m=2 ** 32): - """ Same as random_float_tensor but integers between [low, high) - """ - return torch.floor(random_float_tensor(seed, size, a, c, m) * (high - low)) + low - - @contextmanager def AudioBackendScope(new_backend): previous_backend = torchaudio.get_audio_backend() @@ -65,18 +64,14 @@ def AudioBackendScope(new_backend): def filter_backends_with_mp3(backends): # Filter out backends that do not support mp3 - - test_dirpath, _ = create_temp_assets_dir() - test_filepath = os.path.join( - test_dirpath, "assets", "steam-train-whistle-daniel_simon.mp3" - ) + test_filepath = get_asset_path('steam-train-whistle-daniel_simon.mp3') def supports_mp3(backend): try: with AudioBackendScope(backend): torchaudio.load(test_filepath) return True - except RuntimeError: + except (RuntimeError, ImportError): return False return [backend for backend in backends if supports_mp3(backend)] diff --git a/test/test_batch_consistency.py b/test/test_batch_consistency.py index 69c398bfb6..675a21e029 100644 --- a/test/test_batch_consistency.py +++ b/test/test_batch_consistency.py @@ -1,5 +1,4 @@ """Test numerical consistency among single input and batched input.""" -import os import unittest import torch @@ -7,67 +6,25 @@ import torchaudio.functional as F import common_utils -from common_utils import AudioBackendScope, BACKENDS -def _test_batch_shape(functional, tensor, *args, **kwargs): - - kwargs_compare = {} - if 'atol' in kwargs: - atol = kwargs['atol'] - del kwargs['atol'] - kwargs_compare['atol'] = atol - - if 'rtol' in kwargs: - rtol = kwargs['rtol'] - del kwargs['rtol'] - kwargs_compare['rtol'] = rtol - - # Single then transform then batch - +def _test_batch_consistency(functional, tensor, *args, batch_size=1, atol=1e-8, rtol=1e-5, **kwargs): + # run then batch the result torch.random.manual_seed(42) expected = functional(tensor.clone(), *args, **kwargs) - expected = expected.unsqueeze(0).unsqueeze(0) - - # 1-Batch then transform - tensors = tensor.unsqueeze(0).unsqueeze(0) + expected = expected.repeat([batch_size] + [1] * expected.dim()) + # batch the input and run torch.random.manual_seed(42) - computed = functional(tensors.clone(), *args, **kwargs) - - assert expected.shape == computed.shape, (expected.shape, computed.shape) - assert torch.allclose(expected, computed, **kwargs_compare) - - return tensors, expected - - -def _test_batch(functional, tensor, *args, **kwargs): - tensors, expected = _test_batch_shape(functional, tensor, *args, **kwargs) - - kwargs_compare = {} - if 'atol' in kwargs: - atol = kwargs['atol'] - del kwargs['atol'] - kwargs_compare['atol'] = atol - - if 'rtol' in kwargs: - rtol = kwargs['rtol'] - del kwargs['rtol'] - kwargs_compare['rtol'] = rtol - - # 3-Batch then transform + pattern = [batch_size] + [1] * tensor.dim() + computed = functional(tensor.repeat(pattern), *args, **kwargs) - ind = [3] + [1] * (int(tensors.dim()) - 1) - tensors = tensor.repeat(*ind) + torch.testing.assert_allclose(computed, expected, rtol=rtol, atol=atol) - ind = [3] + [1] * (int(expected.dim()) - 1) - expected = expected.repeat(*ind) - torch.random.manual_seed(42) - computed = functional(tensors.clone(), *args, **kwargs) - - assert expected.shape == computed.shape, (expected.shape, computed.shape) - assert torch.allclose(expected, computed, **kwargs_compare) +def _test_batch(functional, tensor, *args, atol=1e-8, rtol=1e-5, **kwargs): + _test_batch_consistency(functional, tensor, *args, batch_size=1, atol=atol, rtol=rtol, **kwargs) + _test_batch_consistency(functional, tensor, *args, batch_size=3, atol=atol, rtol=rtol, **kwargs) class TestFunctional(unittest.TestCase): @@ -95,7 +52,7 @@ def test_detect_pitch_frequency(self): '440Hz_44100Hz_16bit_05sec.wav', # 1ch ] for filename in filenames: - filepath = os.path.join(common_utils.TEST_DIR_PATH, 'assets', filename) + filepath = common_utils.get_asset_path(filename) waveform, sample_rate = torchaudio.load(filepath) _test_batch(F.detect_pitch_frequency, waveform, sample_rate) @@ -107,6 +64,30 @@ def test_istft(self): ]) _test_batch(F.istft, stft, n_fft=4, length=4) + def test_contrast(self): + waveform = torch.rand(2, 100) - 0.5 + _test_batch(F.contrast, waveform, enhancement_amount=80.) + + def test_dcshift(self): + waveform = torch.rand(2, 100) - 0.5 + _test_batch(F.dcshift, waveform, shift=0.5, limiter_gain=0.05) + + def test_overdrive(self): + waveform = torch.rand(2, 100) - 0.5 + _test_batch(F.overdrive, waveform, gain=45, colour=30) + + def test_sliding_window_cmn(self): + waveform = torch.randn(2, 1024) - 0.5 + _test_batch(F.sliding_window_cmn, waveform, center=True, norm_vars=True) + _test_batch(F.sliding_window_cmn, waveform, center=True, norm_vars=False) + _test_batch(F.sliding_window_cmn, waveform, center=False, norm_vars=True) + _test_batch(F.sliding_window_cmn, waveform, center=False, norm_vars=False) + + def test_vad(self): + filepath = common_utils.get_asset_path("vad-hello-mono-32000.wav") + waveform, sample_rate = torchaudio.load(filepath) + _test_batch(F.vad, waveform, sample_rate=sample_rate) + class TestTransforms(unittest.TestCase): """Test suite for classes defined in `transforms` module""" @@ -119,8 +100,7 @@ def test_batch_AmplitudeToDB(self): # Batch then transform computed = torchaudio.transforms.AmplitudeToDB()(spec.repeat(3, 1, 1)) - assert computed.shape == expected.shape, (computed.shape, expected.shape) - assert torch.allclose(computed, expected) + torch.testing.assert_allclose(computed, expected) def test_batch_Resample(self): waveform = torch.randn(2, 2786) @@ -131,8 +111,7 @@ def test_batch_Resample(self): # Batch then transform computed = torchaudio.transforms.Resample()(waveform.repeat(3, 1, 1)) - assert computed.shape == expected.shape, (computed.shape, expected.shape) - assert torch.allclose(computed, expected) + torch.testing.assert_allclose(computed, expected) def test_batch_MelScale(self): specgram = torch.randn(2, 31, 2786) @@ -144,8 +123,7 @@ def test_batch_MelScale(self): computed = torchaudio.transforms.MelScale()(specgram.repeat(3, 1, 1, 1)) # shape = (3, 2, 201, 1394) - assert computed.shape == expected.shape, (computed.shape, expected.shape) - assert torch.allclose(computed, expected) + torch.testing.assert_allclose(computed, expected) def test_batch_InverseMelScale(self): n_mels = 32 @@ -159,11 +137,10 @@ def test_batch_InverseMelScale(self): computed = torchaudio.transforms.InverseMelScale(n_stft, n_mels)(mel_spec.repeat(3, 1, 1, 1)) # shape = (3, 2, n_mels, 32) - assert computed.shape == expected.shape, (computed.shape, expected.shape) # Because InverseMelScale runs SGD on randomly initialized values so they do not yield # exactly same result. For this reason, tolerance is very relaxed here. - assert torch.allclose(computed, expected, atol=1.0) + torch.testing.assert_allclose(computed, expected, atol=1.0, rtol=1e-5) def test_batch_compute_deltas(self): specgram = torch.randn(2, 31, 2786) @@ -175,12 +152,10 @@ def test_batch_compute_deltas(self): computed = torchaudio.transforms.ComputeDeltas()(specgram.repeat(3, 1, 1, 1)) # shape = (3, 2, 201, 1394) - assert computed.shape == expected.shape, (computed.shape, expected.shape) - assert torch.allclose(computed, expected) + torch.testing.assert_allclose(computed, expected) def test_batch_mulaw(self): - test_filepath = os.path.join( - common_utils.TEST_DIR_PATH, 'assets', 'steam-train-whistle-daniel_simon.wav') + test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') waveform, _ = torchaudio.load(test_filepath) # (2, 278756), 44100 # Single then transform then batch @@ -192,8 +167,7 @@ def test_batch_mulaw(self): computed = torchaudio.transforms.MuLawEncoding()(waveform_batched) # shape = (3, 2, 201, 1394) - assert computed.shape == expected.shape, (computed.shape, expected.shape) - assert torch.allclose(computed, expected) + torch.testing.assert_allclose(computed, expected) # Single then transform then batch waveform_decoded = torchaudio.transforms.MuLawDecoding()(waveform_encoded) @@ -203,12 +177,10 @@ def test_batch_mulaw(self): computed = torchaudio.transforms.MuLawDecoding()(computed) # shape = (3, 2, 201, 1394) - assert computed.shape == expected.shape, (computed.shape, expected.shape) - assert torch.allclose(computed, expected) + torch.testing.assert_allclose(computed, expected) def test_batch_spectrogram(self): - test_filepath = os.path.join( - common_utils.TEST_DIR_PATH, 'assets', 'steam-train-whistle-daniel_simon.wav') + test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') waveform, _ = torchaudio.load(test_filepath) # (2, 278756), 44100 # Single then transform then batch @@ -216,13 +188,10 @@ def test_batch_spectrogram(self): # Batch then transform computed = torchaudio.transforms.Spectrogram()(waveform.repeat(3, 1, 1)) - - assert computed.shape == expected.shape, (computed.shape, expected.shape) - assert torch.allclose(computed, expected) + torch.testing.assert_allclose(computed, expected) def test_batch_melspectrogram(self): - test_filepath = os.path.join( - common_utils.TEST_DIR_PATH, 'assets', 'steam-train-whistle-daniel_simon.wav') + test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') waveform, _ = torchaudio.load(test_filepath) # (2, 278756), 44100 # Single then transform then batch @@ -230,15 +199,10 @@ def test_batch_melspectrogram(self): # Batch then transform computed = torchaudio.transforms.MelSpectrogram()(waveform.repeat(3, 1, 1)) + torch.testing.assert_allclose(computed, expected) - assert computed.shape == expected.shape, (computed.shape, expected.shape) - assert torch.allclose(computed, expected) - - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_batch_mfcc(self): - test_filepath = os.path.join( - common_utils.TEST_DIR_PATH, 'assets', 'steam-train-whistle-daniel_simon.mp3') + test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') waveform, _ = torchaudio.load(test_filepath) # Single then transform then batch @@ -246,13 +210,10 @@ def test_batch_mfcc(self): # Batch then transform computed = torchaudio.transforms.MFCC()(waveform.repeat(3, 1, 1)) - - assert computed.shape == expected.shape, (computed.shape, expected.shape) - assert torch.allclose(computed, expected, atol=1e-5) + torch.testing.assert_allclose(computed, expected, atol=1e-5, rtol=1e-5) def test_batch_TimeStretch(self): - test_filepath = os.path.join( - common_utils.TEST_DIR_PATH, 'assets', 'steam-train-whistle-daniel_simon.wav') + test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') waveform, _ = torchaudio.load(test_filepath) # (2, 278756), 44100 kwargs = { @@ -283,12 +244,10 @@ def test_batch_TimeStretch(self): hop_length=512, )(complex_specgrams.repeat(3, 1, 1, 1, 1)) - assert computed.shape == expected.shape, (computed.shape, expected.shape) - assert torch.allclose(computed, expected, atol=1e-5) + torch.testing.assert_allclose(computed, expected, atol=1e-5, rtol=1e-5) def test_batch_Fade(self): - test_filepath = os.path.join( - common_utils.TEST_DIR_PATH, 'assets', 'steam-train-whistle-daniel_simon.wav') + test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') waveform, _ = torchaudio.load(test_filepath) # (2, 278756), 44100 fade_in_len = 3000 fade_out_len = 3000 @@ -298,13 +257,10 @@ def test_batch_Fade(self): # Batch then transform computed = torchaudio.transforms.Fade(fade_in_len, fade_out_len)(waveform.repeat(3, 1, 1)) - - assert computed.shape == expected.shape, (computed.shape, expected.shape) - assert torch.allclose(computed, expected) + torch.testing.assert_allclose(computed, expected) def test_batch_Vol(self): - test_filepath = os.path.join( - common_utils.TEST_DIR_PATH, 'assets', 'steam-train-whistle-daniel_simon.wav') + test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') waveform, _ = torchaudio.load(test_filepath) # (2, 278756), 44100 # Single then transform then batch @@ -312,9 +268,7 @@ def test_batch_Vol(self): # Batch then transform computed = torchaudio.transforms.Vol(gain=1.1)(waveform.repeat(3, 1, 1)) - - assert computed.shape == expected.shape, (computed.shape, expected.shape) - assert torch.allclose(computed, expected) + torch.testing.assert_allclose(computed, expected) if __name__ == '__main__': diff --git a/test/test_compliance_kaldi.py b/test/test_compliance_kaldi.py index 83751e3e0e..6951ccf59b 100644 --- a/test/test_compliance_kaldi.py +++ b/test/test_compliance_kaldi.py @@ -5,7 +5,9 @@ import torchaudio import torchaudio.compliance.kaldi as kaldi import unittest -from common_utils import AudioBackendScope, BACKENDS, create_temp_assets_dir + +import common_utils +from common_utils import AudioBackendScope, BACKENDS def extract_window(window, wave, f, frame_length, frame_shift, snip_edges): @@ -45,10 +47,9 @@ def first_sample_of_frame(frame, window_size, window_shift, snip_edges): class Test_Kaldi(unittest.TestCase): - test_dirpath, test_dir = create_temp_assets_dir() - test_filepath = os.path.join(test_dirpath, 'assets', 'kaldi_file.wav') - test_8000_filepath = os.path.join(test_dirpath, 'assets', 'kaldi_file_8000.wav') - kaldi_output_dir = os.path.join(test_dirpath, 'assets', 'kaldi') + test_filepath = common_utils.get_asset_path('kaldi_file.wav') + test_8000_filepath = common_utils.get_asset_path('kaldi_file_8000.wav') + kaldi_output_dir = common_utils.get_asset_path('kaldi') test_filepaths = {prefix: [] for prefix in compliance.utils.TEST_PREFIX} # separating test files by their types (e.g 'spec', 'fbank', etc.) @@ -77,7 +78,7 @@ def _test_get_strided_helper(self, num_samples, window_size, window_shift, snip_ for r in range(m): extract_window(window, waveform, r, window_size, window_shift, snip_edges) - self.assertTrue(torch.allclose(window, output)) + torch.testing.assert_allclose(window, output) def test_get_strided(self): # generate any combination where 0 < window_size <= num_samples and @@ -90,8 +91,7 @@ def test_get_strided(self): def _create_data_set(self): # used to generate the dataset to test on. this is not used in testing (offline procedure) - test_dirpath = os.path.dirname(os.path.dirname(os.path.realpath(__file__))) - test_filepath = os.path.join(test_dirpath, 'assets', 'kaldi_file.wav') + test_filepath = common_utils.get_asset_path('kaldi_file.wav') sr = 16000 x = torch.arange(0, 20).float() # between [-6,6] @@ -104,7 +104,7 @@ def _create_data_set(self): sound, sample_rate = torchaudio.load(test_filepath, normalization=False) print(y >> 16) self.assertTrue(sample_rate == sr) - self.assertTrue(torch.allclose(y, sound)) + torch.testing.assert_allclose(y, sound) def _print_diagnostic(self, output, expect_output): # given an output and expected output, it will print the absolute/relative errors (max and mean squared) @@ -156,8 +156,7 @@ def _compliance_test_helper(self, sound_filepath, filepath_key, expected_num_fil output = get_output_fn(sound, args) self._print_diagnostic(output, kaldi_output) - self.assertTrue(output.shape, kaldi_output.shape) - self.assertTrue(torch.allclose(output, kaldi_output, atol=atol, rtol=rtol)) + torch.testing.assert_allclose(output, kaldi_output, atol=atol, rtol=rtol) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -299,7 +298,7 @@ def _test_resample_waveform_accuracy(self, up_scale_factor=None, down_scale_fact ground_truth = ground_truth[..., n_to_trim:-n_to_trim] estimate = estimate[..., n_to_trim:-n_to_trim] - self.assertTrue(torch.allclose(ground_truth, estimate, atol=atol, rtol=rtol)) + torch.testing.assert_allclose(estimate, ground_truth, atol=atol, rtol=rtol) def test_resample_waveform_downsample_accuracy(self): for i in range(1, 20): @@ -324,7 +323,7 @@ def test_resample_waveform_multi_channel(self): for i in range(num_channels): single_channel = sound * (i + 1) * 1.5 single_channel_sampled = kaldi.resample_waveform(single_channel, sample_rate, sample_rate // 2) - self.assertTrue(torch.allclose(multi_sound_sampled[i, :], single_channel_sampled, rtol=1e-4)) + torch.testing.assert_allclose(multi_sound_sampled[i, :], single_channel_sampled[0], rtol=1e-4, atol=1e-8) if __name__ == '__main__': diff --git a/test/test_dataloader.py b/test/test_dataloader.py index 10f861c8b8..602491ee21 100644 --- a/test/test_dataloader.py +++ b/test/test_dataloader.py @@ -1,23 +1,17 @@ import unittest -import torch -import torch.nn as nn -from torch.utils.data import Dataset, DataLoader -import torchaudio -import math -import os -from common_utils import AudioBackendScope, BACKENDS, create_temp_assets_dir +import torchaudio +from torch.utils.data import Dataset, DataLoader -@unittest.skipIf("sox" not in BACKENDS, "sox not available") -class TORCHAUDIODS(Dataset): +import common_utils +from common_utils import AudioBackendScope, BACKENDS - test_dirpath, test_dir = create_temp_assets_dir() +class TORCHAUDIODS(Dataset): def __init__(self): - self.asset_dirpath = os.path.join(self.test_dirpath, "assets") sound_files = ["sinewave.wav", "steam-train-whistle-daniel_simon.mp3"] - self.data = [os.path.join(self.asset_dirpath, fn) for fn in sound_files] - self.si, self.ei = torchaudio.info(os.path.join(self.asset_dirpath, "sinewave.wav")) + self.data = [common_utils.get_asset_path(fn) for fn in sound_files] + self.si, self.ei = torchaudio.info(common_utils.get_asset_path("sinewave.wav")) self.si.precision = 16 self.E = torchaudio.sox_effects.SoxEffectsChain() self.E.append_effect_to_chain("rate", [self.si.rate]) # resample to 16000hz @@ -36,14 +30,6 @@ def __len__(self): @unittest.skipIf("sox" not in BACKENDS, "sox not available") class Test_DataLoader(unittest.TestCase): - @classmethod - def setUpClass(cls): - torchaudio.initialize_sox() - - @classmethod - def tearDownClass(cls): - torchaudio.shutdown_sox() - def test_1(self): expected_size = (2, 1, 16000) ds = TORCHAUDIODS() @@ -51,6 +37,7 @@ def test_1(self): for x in dl: self.assertTrue(x.size() == expected_size) + if __name__ == '__main__': with AudioBackendScope("sox"): unittest.main() diff --git a/test/test_datasets.py b/test/test_datasets.py index 4aa35b2063..e42e21e999 100644 --- a/test/test_datasets.py +++ b/test/test_datasets.py @@ -1,4 +1,3 @@ -import os import unittest from torchaudio.datasets.commonvoice import COMMONVOICE @@ -13,8 +12,7 @@ class TestDatasets(unittest.TestCase): - test_dirpath, test_dir = common_utils.create_temp_assets_dir() - path = os.path.join(test_dirpath, "assets") + path = common_utils.get_asset_path() def test_yesno(self): data = YESNO(self.path) diff --git a/test/test_functional.py b/test/test_functional.py index f779d8988d..a126bd9581 100644 --- a/test/test_functional.py +++ b/test/test_functional.py @@ -1,5 +1,4 @@ import math -import os import unittest import torch @@ -10,14 +9,49 @@ import common_utils +class _LfilterMixin: + device = None + dtype = None + + def test_simple(self): + """ + Create a very basic signal, + Then make a simple 4th order delay + The output should be same as the input but shifted + """ + + torch.random.manual_seed(42) + waveform = torch.rand(2, 44100 * 1, dtype=self.dtype, device=self.device) + b_coeffs = torch.tensor([0, 0, 0, 1], dtype=self.dtype, device=self.device) + a_coeffs = torch.tensor([1, 0, 0, 0], dtype=self.dtype, device=self.device) + output_waveform = F.lfilter(waveform, a_coeffs, b_coeffs) + + torch.testing.assert_allclose(output_waveform[:, 3:], waveform[:, 0:-3], atol=1e-5, rtol=1e-5) + + +class TestLfilterFloat32CPU(_LfilterMixin, unittest.TestCase): + device = torch.device('cpu') + dtype = torch.float32 + + +class TestLfilterFloat64CPU(_LfilterMixin, unittest.TestCase): + device = torch.device('cpu') + dtype = torch.float64 + + +@unittest.skipIf(not torch.cuda.is_available(), "CUDA not available") +class TestLfilterFloat32CUDA(_LfilterMixin, unittest.TestCase): + device = torch.device('cuda') + dtype = torch.float32 + + class TestComputeDeltas(unittest.TestCase): """Test suite for correctness of compute_deltas""" def test_one_channel(self): specgram = torch.tensor([[[1.0, 2.0, 3.0, 4.0]]]) expected = torch.tensor([[[0.5, 1.0, 1.0, 0.5]]]) computed = F.compute_deltas(specgram, win_length=3) - assert computed.shape == expected.shape, (computed.shape, expected.shape) - assert torch.allclose(computed, expected) + torch.testing.assert_allclose(computed, expected) def test_two_channels(self): specgram = torch.tensor([[[1.0, 2.0, 3.0, 4.0], @@ -25,16 +59,13 @@ def test_two_channels(self): expected = torch.tensor([[[0.5, 1.0, 1.0, 0.5], [0.5, 1.0, 1.0, 0.5]]]) computed = F.compute_deltas(specgram, win_length=3) - assert computed.shape == expected.shape, (computed.shape, expected.shape) - assert torch.allclose(computed, expected) + torch.testing.assert_allclose(computed, expected) def _compare_estimate(sound, estimate, atol=1e-6, rtol=1e-8): # trim sound for case when constructed signal is shorter than original sound = sound[..., :estimate.size(-1)] - - assert sound.shape == estimate.shape, (sound.shape, estimate.shape) - assert torch.allclose(sound, estimate, atol=atol, rtol=rtol) + torch.testing.assert_allclose(estimate, sound, atol=atol, rtol=rtol) def _test_istft_is_inverse_of_stft(kwargs): @@ -269,10 +300,8 @@ def test_linearity_of_istft4(self): class TestDetectPitchFrequency(unittest.TestCase): def test_pitch(self): - test_filepath_100 = os.path.join( - common_utils.TEST_DIR_PATH, 'assets', "100Hz_44100Hz_16bit_05sec.wav") - test_filepath_440 = os.path.join( - common_utils.TEST_DIR_PATH, 'assets', "440Hz_44100Hz_16bit_05sec.wav") + test_filepath_100 = common_utils.get_asset_path("100Hz_44100Hz_16bit_05sec.wav") + test_filepath_440 = common_utils.get_asset_path("440Hz_44100Hz_16bit_05sec.wav") # Files from https://www.mediacollege.com/audio/tone/download/ tests = [ @@ -308,13 +337,13 @@ def test_DB_to_amplitude(self): db = F.amplitude_to_DB(torch.abs(x), multiplier, amin, db_multiplier, top_db=None) x2 = F.DB_to_amplitude(db, ref, power) - self.assertTrue(torch.allclose(torch.abs(x), x2, atol=5e-5)) + torch.testing.assert_allclose(x2, torch.abs(x), atol=5e-5, rtol=1e-5) # Spectrogram amplitude -> DB -> amplitude db = F.amplitude_to_DB(spec, multiplier, amin, db_multiplier, top_db=None) x2 = F.DB_to_amplitude(db, ref, power) - self.assertTrue(torch.allclose(spec, x2, atol=5e-5)) + torch.testing.assert_allclose(x2, spec, atol=5e-5, rtol=1e-5) # Waveform power -> DB -> power multiplier = 10. @@ -323,13 +352,13 @@ def test_DB_to_amplitude(self): db = F.amplitude_to_DB(x, multiplier, amin, db_multiplier, top_db=None) x2 = F.DB_to_amplitude(db, ref, power) - self.assertTrue(torch.allclose(torch.abs(x), x2, atol=5e-5)) + torch.testing.assert_allclose(x2, torch.abs(x), atol=5e-5, rtol=1e-5) # Spectrogram power -> DB -> power db = F.amplitude_to_DB(spec, multiplier, amin, db_multiplier, top_db=None) x2 = F.DB_to_amplitude(db, ref, power) - self.assertTrue(torch.allclose(spec, x2, atol=5e-5)) + torch.testing.assert_allclose(x2, spec, atol=5e-5, rtol=1e-5) @pytest.mark.parametrize('complex_tensor', [ @@ -341,7 +370,7 @@ def test_complex_norm(complex_tensor, power): expected_norm_tensor = complex_tensor.pow(2).sum(-1).pow(power / 2) norm_tensor = F.complex_norm(complex_tensor, power) - assert torch.allclose(expected_norm_tensor, norm_tensor, atol=1e-5) + torch.testing.assert_allclose(norm_tensor, expected_norm_tensor, atol=1e-5, rtol=1e-5) @pytest.mark.parametrize('specgram', [ @@ -365,13 +394,12 @@ def test_mask_along_axis(specgram, mask_param, mask_value, axis): assert num_masked_columns < mask_param -@pytest.mark.parametrize('specgrams', [ - torch.randn(4, 2, 1025, 400), -]) @pytest.mark.parametrize('mask_param', [100]) @pytest.mark.parametrize('mask_value', [0., 30.]) @pytest.mark.parametrize('axis', [2, 3]) -def test_mask_along_axis_iid(specgrams, mask_param, mask_value, axis): +def test_mask_along_axis_iid(mask_param, mask_value, axis): + torch.random.manual_seed(42) + specgrams = torch.randn(4, 2, 1025, 400) mask_specgrams = F.mask_along_axis_iid(specgrams, mask_param, mask_value, axis) diff --git a/test/test.py b/test/test_io.py similarity index 100% rename from test/test.py rename to test/test_io.py diff --git a/test/test_kaldi_io.py b/test/test_kaldi_io.py index f4ff557e56..bc9903a3ef 100644 --- a/test/test_kaldi_io.py +++ b/test/test_kaldi_io.py @@ -1,21 +1,21 @@ -import os +import unittest + import torch import torchaudio.kaldi_io as kio -import unittest + import common_utils class Test_KaldiIO(unittest.TestCase): data1 = [[1, 2, 3], [11, 12, 13], [21, 22, 23]] data2 = [[31, 32, 33], [41, 42, 43], [51, 52, 53]] - test_dirpath, test_dir = common_utils.create_temp_assets_dir() def _test_helper(self, file_name, expected_data, fn, expected_dtype): """ Takes a file_name to the input data and a function fn to extract the data. It compares the extracted data to the expected_data. The expected_dtype will be used to check that the extracted data is of the right type. """ - test_filepath = os.path.join(self.test_dirpath, "assets", file_name) + test_filepath = common_utils.get_asset_path(file_name) expected_output = {'key' + str(idx + 1): torch.tensor(val, dtype=expected_dtype) for idx, val in enumerate(expected_data)} diff --git a/test/test_librosa_compatibility.py b/test/test_librosa_compatibility.py index 36bdb7a195..460f379ce9 100644 --- a/test/test_librosa_compatibility.py +++ b/test/test_librosa_compatibility.py @@ -51,7 +51,7 @@ def test_griffinlim(self): momentum=momentum, init=init, length=length) lr_out = torch.from_numpy(lr_out).unsqueeze(0) - assert torch.allclose(ta_out, lr_out, atol=5e-5) + torch.testing.assert_allclose(ta_out, lr_out, atol=5e-5, rtol=1e-5) def _test_create_fb(self, n_mels=40, sample_rate=22050, n_fft=2048, fmin=0.0, fmax=8000.0): librosa_fb = librosa.filters.mel(sr=sample_rate, @@ -68,7 +68,8 @@ def _test_create_fb(self, n_mels=40, sample_rate=22050, n_fft=2048, fmin=0.0, fm n_freqs=(n_fft // 2 + 1)) for i_mel_bank in range(n_mels): - assert torch.allclose(fb[:, i_mel_bank], torch.tensor(librosa_fb[i_mel_bank]), atol=1e-4) + torch.testing.assert_allclose(fb[:, i_mel_bank], torch.tensor(librosa_fb[i_mel_bank]), + atol=1e-4, rtol=1e-5) def test_create_fb(self): self._test_create_fb() @@ -91,18 +92,18 @@ def test_amplitude_to_DB(self): ta_out = F.amplitude_to_DB(spec, multiplier, amin, db_multiplier, top_db) lr_out = librosa.core.power_to_db(spec.numpy()) - lr_out = torch.from_numpy(lr_out).unsqueeze(0) + lr_out = torch.from_numpy(lr_out) - assert torch.allclose(ta_out, lr_out, atol=5e-5) + torch.testing.assert_allclose(ta_out, lr_out, atol=5e-5, rtol=1e-5) # Amplitude to DB multiplier = 20.0 ta_out = F.amplitude_to_DB(spec, multiplier, amin, db_multiplier, top_db) lr_out = librosa.core.amplitude_to_db(spec.numpy()) - lr_out = torch.from_numpy(lr_out).unsqueeze(0) + lr_out = torch.from_numpy(lr_out) - assert torch.allclose(ta_out, lr_out, atol=5e-5) + torch.testing.assert_allclose(ta_out, lr_out, atol=5e-5, rtol=1e-5) @pytest.mark.parametrize('complex_specgrams', [ @@ -148,7 +149,7 @@ def test_phase_vocoder(complex_specgrams, rate, hop_length): def _load_audio_asset(*asset_paths, **kwargs): - file_path = os.path.join(common_utils.TEST_DIR_PATH, 'assets', *asset_paths) + file_path = common_utils.get_asset_path(*asset_paths) sound, sample_rate = torchaudio.load(file_path, **kwargs) return sound, sample_rate @@ -164,7 +165,7 @@ def _test_compatibilities(n_fft, hop_length, power, n_mels, n_mfcc, sample_rate) y=sound_librosa, n_fft=n_fft, hop_length=hop_length, power=power) out_torch = spect_transform(sound).squeeze().cpu() - assert torch.allclose(out_torch, torch.from_numpy(out_librosa), atol=1e-5) + torch.testing.assert_allclose(out_torch, torch.from_numpy(out_librosa), atol=1e-5, rtol=1e-5) # test mel spectrogram melspect_transform = torchaudio.transforms.MelSpectrogram( @@ -175,25 +176,25 @@ def _test_compatibilities(n_fft, hop_length, power, n_mels, n_mfcc, sample_rate) hop_length=hop_length, n_mels=n_mels, htk=True, norm=None) librosa_mel_tensor = torch.from_numpy(librosa_mel) torch_mel = melspect_transform(sound).squeeze().cpu() - assert torch.allclose( - torch_mel.type(librosa_mel_tensor.dtype), librosa_mel_tensor, atol=5e-3) + torch.testing.assert_allclose( + torch_mel.type(librosa_mel_tensor.dtype), librosa_mel_tensor, atol=5e-3, rtol=1e-5) # test s2db power_to_db_transform = torchaudio.transforms.AmplitudeToDB('power', 80.) power_to_db_torch = power_to_db_transform(spect_transform(sound)).squeeze().cpu() power_to_db_librosa = librosa.core.spectrum.power_to_db(out_librosa) - assert torch.allclose(power_to_db_torch, torch.from_numpy(power_to_db_librosa), atol=5e-3) + torch.testing.assert_allclose(power_to_db_torch, torch.from_numpy(power_to_db_librosa), atol=5e-3, rtol=1e-5) mag_to_db_transform = torchaudio.transforms.AmplitudeToDB('magnitude', 80.) mag_to_db_torch = mag_to_db_transform(torch.abs(sound)).squeeze().cpu() mag_to_db_librosa = librosa.core.spectrum.amplitude_to_db(sound_librosa) - assert torch.allclose(mag_to_db_torch, torch.from_numpy(mag_to_db_librosa), atol=5e-3) + torch.testing.assert_allclose(mag_to_db_torch, torch.from_numpy(mag_to_db_librosa), atol=5e-3, rtol=1e-5) power_to_db_torch = power_to_db_transform(melspect_transform(sound)).squeeze().cpu() db_librosa = librosa.core.spectrum.power_to_db(librosa_mel) db_librosa_tensor = torch.from_numpy(db_librosa) - assert torch.allclose( - power_to_db_torch.type(db_librosa_tensor.dtype), db_librosa_tensor, atol=5e-3) + torch.testing.assert_allclose( + power_to_db_torch.type(db_librosa_tensor.dtype), db_librosa_tensor, atol=5e-3, rtol=1e-5) # test MFCC melkwargs = {'hop_length': hop_length, 'n_fft': n_fft} @@ -214,8 +215,8 @@ def _test_compatibilities(n_fft, hop_length, power, n_mels, n_mfcc, sample_rate) librosa_mfcc_tensor = torch.from_numpy(librosa_mfcc) torch_mfcc = mfcc_transform(sound).squeeze().cpu() - assert torch.allclose( - torch_mfcc.type(librosa_mfcc_tensor.dtype), librosa_mfcc_tensor, atol=5e-3) + torch.testing.assert_allclose( + torch_mfcc.type(librosa_mfcc_tensor.dtype), librosa_mfcc_tensor, atol=5e-3, rtol=1e-5) class TestTransforms(_LibrosaMixin, unittest.TestCase): @@ -242,10 +243,8 @@ def test_basics2(self): } _test_compatibilities(**kwargs) - # NOTE: Test passes offline, but fails on TravisCI, see #372. - @unittest.skipIf( - os.environ.get('CI') == 'true' and os.environ.get('TRAVIS') == 'true', - 'Test is known to fail on TravisCI') + # NOTE: Test passes offline, but fails on TravisCI (and CircleCI), see #372. + @unittest.skipIf('CI' in os.environ, 'Test is known to fail on CI') def test_basics3(self): kwargs = { 'n_fft': 200, @@ -289,7 +288,7 @@ def test_MelScale(self): S=spec_lr, sr=sample_rate, n_fft=n_fft, hop_length=hop_length, win_length=n_fft, center=True, window='hann', n_mels=n_mels, htk=True, norm=None) # Note: Using relaxed rtol instead of atol - assert torch.allclose(melspec_ta, torch.from_numpy(melspec_lr[None, ...]), rtol=1e-3) + torch.testing.assert_allclose(melspec_ta, torch.from_numpy(melspec_lr[None, ...]), atol=1e-8, rtol=1e-3) def test_InverseMelScale(self): """InverseMelScale transform is comparable to that of librosa""" @@ -332,7 +331,7 @@ def test_InverseMelScale(self): # https://github.com/pytorch/audio/pull/366 for the discussion of the choice of algorithm # https://github.com/pytorch/audio/pull/448/files#r385747021 for the distribution of P-inf # distance over frequencies. - assert torch.allclose(spec_ta, spec_lr, atol=threshold) + torch.testing.assert_allclose(spec_ta, spec_lr, atol=threshold, rtol=1e-5) threshold = 1700.0 # This threshold was choosen empirically, based on the following observations diff --git a/test/test_functional_filtering.py b/test/test_sox_compatibility.py similarity index 54% rename from test/test_functional_filtering.py rename to test/test_sox_compatibility.py index c02abbd8d2..508ec7abfa 100644 --- a/test/test_functional_filtering.py +++ b/test/test_sox_compatibility.py @@ -1,4 +1,3 @@ -import os import unittest import torch @@ -6,102 +5,15 @@ import torchaudio.functional as F import torchaudio.transforms as T -from common_utils import AudioBackendScope, BACKENDS, create_temp_assets_dir +import common_utils +from common_utils import AudioBackendScope, BACKENDS class TestFunctionalFiltering(unittest.TestCase): - test_dirpath, test_dir = create_temp_assets_dir() - - def _test_lfilter_basic(self, dtype, device): - """ - Create a very basic signal, - Then make a simple 4th order delay - The output should be same as the input but shifted - """ - - torch.random.manual_seed(42) - waveform = torch.rand(2, 44100 * 1, dtype=dtype, device=device) - b_coeffs = torch.tensor([0, 0, 0, 1], dtype=dtype, device=device) - a_coeffs = torch.tensor([1, 0, 0, 0], dtype=dtype, device=device) - output_waveform = F.lfilter(waveform, a_coeffs, b_coeffs) - - assert torch.allclose(waveform[:, 0:-3], output_waveform[:, 3:], atol=1e-5) - - def test_lfilter_basic(self): - self._test_lfilter_basic(torch.float32, torch.device("cpu")) - - def test_lfilter_basic_double(self): - self._test_lfilter_basic(torch.float64, torch.device("cpu")) - - def test_lfilter_basic_gpu(self): - if torch.cuda.is_available(): - self._test_lfilter_basic(torch.float32, torch.device("cuda:0")) - else: - print("skipping GPU test for lfilter_basic because device not available") - pass - - def _test_lfilter(self, waveform, device): - """ - Design an IIR lowpass filter using scipy.signal filter design - https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.iirdesign.html#scipy.signal.iirdesign - - Example - >>> from scipy.signal import iirdesign - >>> b, a = iirdesign(0.2, 0.3, 1, 60) - """ - - b_coeffs = torch.tensor( - [ - 0.00299893, - -0.0051152, - 0.00841964, - -0.00747802, - 0.00841964, - -0.0051152, - 0.00299893, - ], - device=device, - ) - a_coeffs = torch.tensor( - [ - 1.0, - -4.8155751, - 10.2217618, - -12.14481273, - 8.49018171, - -3.3066882, - 0.56088705, - ], - device=device, - ) - - output_waveform = F.lfilter(waveform, a_coeffs, b_coeffs) - assert len(output_waveform.size()) == 2 - assert output_waveform.size(0) == waveform.size(0) - assert output_waveform.size(1) == waveform.size(1) - - def test_lfilter(self): - - filepath = os.path.join(self.test_dirpath, "assets", "whitenoise.wav") - waveform, _ = torchaudio.load(filepath, normalization=True) - - self._test_lfilter(waveform, torch.device("cpu")) - - def test_lfilter_gpu(self): - if torch.cuda.is_available(): - filepath = os.path.join(self.test_dirpath, "assets", "whitenoise.wav") - waveform, _ = torchaudio.load(filepath, normalization=True) - cuda0 = torch.device("cuda:0") - cuda_waveform = waveform.cuda(device=cuda0) - self._test_lfilter(cuda_waveform, cuda0) - else: - print("skipping GPU test for lfilter because device not available") - pass - @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") def test_gain(self): - test_filepath = os.path.join(self.test_dirpath, "assets", "steam-train-whistle-daniel_simon.wav") + test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') waveform, _ = torchaudio.load(test_filepath) waveform_gain = F.gain(waveform, 3) @@ -112,12 +24,12 @@ def test_gain(self): E.append_effect_to_chain("gain", [3]) sox_gain_waveform = E.sox_build_flow_effects()[0] - assert torch.allclose(waveform_gain, sox_gain_waveform, atol=1e-04) + torch.testing.assert_allclose(waveform_gain, sox_gain_waveform, atol=1e-04, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") def test_dither(self): - test_filepath = os.path.join(self.test_dirpath, "assets", "steam-train-whistle-daniel_simon.wav") + test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') waveform, _ = torchaudio.load(test_filepath) waveform_dithered = F.dither(waveform) @@ -128,18 +40,18 @@ def test_dither(self): E.append_effect_to_chain("dither", []) sox_dither_waveform = E.sox_build_flow_effects()[0] - assert torch.allclose(waveform_dithered, sox_dither_waveform, atol=1e-04) + torch.testing.assert_allclose(waveform_dithered, sox_dither_waveform, atol=1e-04, rtol=1e-5) E.clear_chain() E.append_effect_to_chain("dither", ["-s"]) sox_dither_waveform_ns = E.sox_build_flow_effects()[0] - assert torch.allclose(waveform_dithered_noiseshaped, sox_dither_waveform_ns, atol=1e-02) + torch.testing.assert_allclose(waveform_dithered_noiseshaped, sox_dither_waveform_ns, atol=1e-02, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") def test_vctk_transform_pipeline(self): - test_filepath_vctk = os.path.join(self.test_dirpath, "assets/VCTK-Corpus/wav48/p224/", "p224_002.wav") + test_filepath_vctk = common_utils.get_asset_path('VCTK-Corpus', 'wav48', 'p224', 'p224_002.wav') wf_vctk, sr_vctk = torchaudio.load(test_filepath_vctk) # rate @@ -157,28 +69,27 @@ def test_vctk_transform_pipeline(self): E.append_effect_to_chain("dither", ["-s"]) wf_vctk_sox = E.sox_build_flow_effects()[0] - assert torch.allclose(wf_vctk, wf_vctk_sox, rtol=1e-03, atol=1e-03) + torch.testing.assert_allclose(wf_vctk, wf_vctk_sox, rtol=1e-03, atol=1e-03) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") def test_lowpass(self): - """ Test biquad lowpass filter, compare to SoX implementation """ - CUTOFF_FREQ = 3000 + cutoff_freq = 3000 - noise_filepath = os.path.join(self.test_dirpath, "assets", "whitenoise.wav") + noise_filepath = common_utils.get_asset_path('whitenoise.wav') E = torchaudio.sox_effects.SoxEffectsChain() E.set_input_file(noise_filepath) - E.append_effect_to_chain("lowpass", [CUTOFF_FREQ]) + E.append_effect_to_chain("lowpass", [cutoff_freq]) sox_output_waveform, sr = E.sox_build_flow_effects() waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.lowpass_biquad(waveform, sample_rate, CUTOFF_FREQ) + output_waveform = F.lowpass_biquad(waveform, sample_rate, cutoff_freq) - assert torch.allclose(sox_output_waveform, output_waveform, atol=1e-4) + torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -187,19 +98,19 @@ def test_highpass(self): Test biquad highpass filter, compare to SoX implementation """ - CUTOFF_FREQ = 2000 + cutoff_freq = 2000 - noise_filepath = os.path.join(self.test_dirpath, "assets", "whitenoise.wav") + noise_filepath = common_utils.get_asset_path('whitenoise.wav') E = torchaudio.sox_effects.SoxEffectsChain() E.set_input_file(noise_filepath) - E.append_effect_to_chain("highpass", [CUTOFF_FREQ]) + E.append_effect_to_chain("highpass", [cutoff_freq]) sox_output_waveform, sr = E.sox_build_flow_effects() waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.highpass_biquad(waveform, sample_rate, CUTOFF_FREQ) + output_waveform = F.highpass_biquad(waveform, sample_rate, cutoff_freq) # TBD - this fails at the 1e-4 level, debug why - assert torch.allclose(sox_output_waveform, output_waveform, atol=1e-3) + torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-3, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -208,19 +119,19 @@ def test_allpass(self): Test biquad allpass filter, compare to SoX implementation """ - CENTRAL_FREQ = 1000 - Q = 0.707 + central_freq = 1000 + q = 0.707 - noise_filepath = os.path.join(self.test_dirpath, "assets", "whitenoise.wav") + noise_filepath = common_utils.get_asset_path('whitenoise.wav') E = torchaudio.sox_effects.SoxEffectsChain() E.set_input_file(noise_filepath) - E.append_effect_to_chain("allpass", [CENTRAL_FREQ, str(Q) + 'q']) + E.append_effect_to_chain("allpass", [central_freq, str(q) + 'q']) sox_output_waveform, sr = E.sox_build_flow_effects() waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.allpass_biquad(waveform, sample_rate, CENTRAL_FREQ, Q) + output_waveform = F.allpass_biquad(waveform, sample_rate, central_freq, q) - assert torch.allclose(sox_output_waveform, output_waveform, atol=1e-4) + torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -229,20 +140,20 @@ def test_bandpass_with_csg(self): Test biquad bandpass filter, compare to SoX implementation """ - CENTRAL_FREQ = 1000 - Q = 0.707 - CONST_SKIRT_GAIN = True + central_freq = 1000 + q = 0.707 + const_skirt_gain = True - noise_filepath = os.path.join(self.test_dirpath, "assets", "whitenoise.wav") + noise_filepath = common_utils.get_asset_path('whitenoise.wav') E = torchaudio.sox_effects.SoxEffectsChain() E.set_input_file(noise_filepath) - E.append_effect_to_chain("bandpass", ["-c", CENTRAL_FREQ, str(Q) + 'q']) + E.append_effect_to_chain("bandpass", ["-c", central_freq, str(q) + 'q']) sox_output_waveform, sr = E.sox_build_flow_effects() waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.bandpass_biquad(waveform, sample_rate, CENTRAL_FREQ, Q, CONST_SKIRT_GAIN) + output_waveform = F.bandpass_biquad(waveform, sample_rate, central_freq, q, const_skirt_gain) - assert torch.allclose(sox_output_waveform, output_waveform, atol=1e-4) + torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -251,20 +162,20 @@ def test_bandpass_without_csg(self): Test biquad bandpass filter, compare to SoX implementation """ - CENTRAL_FREQ = 1000 - Q = 0.707 - CONST_SKIRT_GAIN = False + central_freq = 1000 + q = 0.707 + const_skirt_gain = False - noise_filepath = os.path.join(self.test_dirpath, "assets", "whitenoise.wav") + noise_filepath = common_utils.get_asset_path('whitenoise.wav') E = torchaudio.sox_effects.SoxEffectsChain() E.set_input_file(noise_filepath) - E.append_effect_to_chain("bandpass", [CENTRAL_FREQ, str(Q) + 'q']) + E.append_effect_to_chain("bandpass", [central_freq, str(q) + 'q']) sox_output_waveform, sr = E.sox_build_flow_effects() waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.bandpass_biquad(waveform, sample_rate, CENTRAL_FREQ, Q, CONST_SKIRT_GAIN) + output_waveform = F.bandpass_biquad(waveform, sample_rate, central_freq, q, const_skirt_gain) - assert torch.allclose(sox_output_waveform, output_waveform, atol=1e-4) + torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -273,19 +184,19 @@ def test_bandreject(self): Test biquad bandreject filter, compare to SoX implementation """ - CENTRAL_FREQ = 1000 - Q = 0.707 + central_freq = 1000 + q = 0.707 - noise_filepath = os.path.join(self.test_dirpath, "assets", "whitenoise.wav") + noise_filepath = common_utils.get_asset_path('whitenoise.wav') E = torchaudio.sox_effects.SoxEffectsChain() E.set_input_file(noise_filepath) - E.append_effect_to_chain("bandreject", [CENTRAL_FREQ, str(Q) + 'q']) + E.append_effect_to_chain("bandreject", [central_freq, str(q) + 'q']) sox_output_waveform, sr = E.sox_build_flow_effects() waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.bandreject_biquad(waveform, sample_rate, CENTRAL_FREQ, Q) + output_waveform = F.bandreject_biquad(waveform, sample_rate, central_freq, q) - assert torch.allclose(sox_output_waveform, output_waveform, atol=1e-4) + torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -294,20 +205,20 @@ def test_band_with_noise(self): Test biquad band filter with noise mode, compare to SoX implementation """ - CENTRAL_FREQ = 1000 - Q = 0.707 - NOISE = True + central_freq = 1000 + q = 0.707 + noise = True - noise_filepath = os.path.join(self.test_dirpath, "assets", "whitenoise.wav") + noise_filepath = common_utils.get_asset_path('whitenoise.wav') E = torchaudio.sox_effects.SoxEffectsChain() E.set_input_file(noise_filepath) - E.append_effect_to_chain("band", ["-n", CENTRAL_FREQ, str(Q) + 'q']) + E.append_effect_to_chain("band", ["-n", central_freq, str(q) + 'q']) sox_output_waveform, sr = E.sox_build_flow_effects() waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.band_biquad(waveform, sample_rate, CENTRAL_FREQ, Q, NOISE) + output_waveform = F.band_biquad(waveform, sample_rate, central_freq, q, noise) - assert torch.allclose(sox_output_waveform, output_waveform, atol=1e-4) + torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -316,20 +227,20 @@ def test_band_without_noise(self): Test biquad band filter without noise mode, compare to SoX implementation """ - CENTRAL_FREQ = 1000 - Q = 0.707 - NOISE = False + central_freq = 1000 + q = 0.707 + noise = False - noise_filepath = os.path.join(self.test_dirpath, "assets", "whitenoise.wav") + noise_filepath = common_utils.get_asset_path('whitenoise.wav') E = torchaudio.sox_effects.SoxEffectsChain() E.set_input_file(noise_filepath) - E.append_effect_to_chain("band", [CENTRAL_FREQ, str(Q) + 'q']) + E.append_effect_to_chain("band", [central_freq, str(q) + 'q']) sox_output_waveform, sr = E.sox_build_flow_effects() waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.band_biquad(waveform, sample_rate, CENTRAL_FREQ, Q, NOISE) + output_waveform = F.band_biquad(waveform, sample_rate, central_freq, q, noise) - assert torch.allclose(sox_output_waveform, output_waveform, atol=1e-4) + torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -338,20 +249,20 @@ def test_treble(self): Test biquad treble filter, compare to SoX implementation """ - CENTRAL_FREQ = 1000 - Q = 0.707 - GAIN = 40 + central_freq = 1000 + q = 0.707 + gain = 40 - noise_filepath = os.path.join(self.test_dirpath, "assets", "whitenoise.wav") + noise_filepath = common_utils.get_asset_path('whitenoise.wav') E = torchaudio.sox_effects.SoxEffectsChain() E.set_input_file(noise_filepath) - E.append_effect_to_chain("treble", [GAIN, CENTRAL_FREQ, str(Q) + 'q']) + E.append_effect_to_chain("treble", [gain, central_freq, str(q) + 'q']) sox_output_waveform, sr = E.sox_build_flow_effects() waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.treble_biquad(waveform, sample_rate, GAIN, CENTRAL_FREQ, Q) + output_waveform = F.treble_biquad(waveform, sample_rate, gain, central_freq, q) - assert torch.allclose(sox_output_waveform, output_waveform, atol=1e-4) + torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -360,7 +271,7 @@ def test_deemph(self): Test biquad deemph filter, compare to SoX implementation """ - noise_filepath = os.path.join(self.test_dirpath, "assets", "whitenoise.wav") + noise_filepath = common_utils.get_asset_path('whitenoise.wav') E = torchaudio.sox_effects.SoxEffectsChain() E.set_input_file(noise_filepath) E.append_effect_to_chain("deemph") @@ -369,7 +280,7 @@ def test_deemph(self): waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) output_waveform = F.deemph_biquad(waveform, sample_rate) - assert torch.allclose(sox_output_waveform, output_waveform, atol=1e-4) + torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -378,7 +289,7 @@ def test_riaa(self): Test biquad riaa filter, compare to SoX implementation """ - noise_filepath = os.path.join(self.test_dirpath, "assets", "whitenoise.wav") + noise_filepath = common_utils.get_asset_path('whitenoise.wav') E = torchaudio.sox_effects.SoxEffectsChain() E.set_input_file(noise_filepath) E.append_effect_to_chain("riaa") @@ -387,7 +298,81 @@ def test_riaa(self): waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) output_waveform = F.riaa_biquad(waveform, sample_rate) - assert torch.allclose(sox_output_waveform, output_waveform, atol=1e-4) + torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") + def test_contrast(self): + """ + Test contrast effect, compare to SoX implementation + """ + enhancement_amount = 80. + noise_filepath = common_utils.get_asset_path('whitenoise.wav') + E = torchaudio.sox_effects.SoxEffectsChain() + E.set_input_file(noise_filepath) + E.append_effect_to_chain("contrast", [enhancement_amount]) + sox_output_waveform, sr = E.sox_build_flow_effects() + + waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) + output_waveform = F.contrast(waveform, enhancement_amount) + + torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") + def test_dcshift_with_limiter(self): + """ + Test dcshift effect, compare to SoX implementation + """ + shift = 0.5 + limiter_gain = 0.05 + noise_filepath = common_utils.get_asset_path('whitenoise.wav') + E = torchaudio.sox_effects.SoxEffectsChain() + E.set_input_file(noise_filepath) + E.append_effect_to_chain("dcshift", [shift, limiter_gain]) + sox_output_waveform, sr = E.sox_build_flow_effects() + + waveform, _ = torchaudio.load(noise_filepath, normalization=True) + output_waveform = F.dcshift(waveform, shift, limiter_gain) + + torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") + def test_dcshift_without_limiter(self): + """ + Test dcshift effect, compare to SoX implementation + """ + shift = 0.6 + noise_filepath = common_utils.get_asset_path('whitenoise.wav') + E = torchaudio.sox_effects.SoxEffectsChain() + E.set_input_file(noise_filepath) + E.append_effect_to_chain("dcshift", [shift]) + sox_output_waveform, sr = E.sox_build_flow_effects() + + waveform, _ = torchaudio.load(noise_filepath, normalization=True) + output_waveform = F.dcshift(waveform, shift) + + torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") + def test_overdrive(self): + """ + Test overdrive effect, compare to SoX implementation + """ + gain = 30 + colour = 40 + noise_filepath = common_utils.get_asset_path('whitenoise.wav') + E = torchaudio.sox_effects.SoxEffectsChain() + E.set_input_file(noise_filepath) + E.append_effect_to_chain("overdrive", [gain, colour]) + sox_output_waveform, sr = E.sox_build_flow_effects() + + waveform, _ = torchaudio.load(noise_filepath, normalization=True) + output_waveform = F.overdrive(waveform, gain, colour) + + torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -396,26 +381,26 @@ def test_equalizer(self): Test biquad peaking equalizer filter, compare to SoX implementation """ - CENTER_FREQ = 300 - Q = 0.707 - GAIN = 1 + center_freq = 300 + q = 0.707 + gain = 1 - noise_filepath = os.path.join(self.test_dirpath, "assets", "whitenoise.wav") + noise_filepath = common_utils.get_asset_path('whitenoise.wav') E = torchaudio.sox_effects.SoxEffectsChain() E.set_input_file(noise_filepath) - E.append_effect_to_chain("equalizer", [CENTER_FREQ, Q, GAIN]) + E.append_effect_to_chain("equalizer", [center_freq, q, gain]) sox_output_waveform, sr = E.sox_build_flow_effects() waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.equalizer_biquad(waveform, sample_rate, CENTER_FREQ, GAIN, Q) + output_waveform = F.equalizer_biquad(waveform, sample_rate, center_freq, gain, q) - assert torch.allclose(sox_output_waveform, output_waveform, atol=1e-4) + torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") def test_perf_biquad_filtering(self): - fn_sine = os.path.join(self.test_dirpath, "assets", "whitenoise.wav") + fn_sine = common_utils.get_asset_path('whitenoise.wav') b0 = 0.4 b1 = 0.2 @@ -435,7 +420,7 @@ def test_perf_biquad_filtering(self): waveform, torch.tensor([a0, a1, a2]), torch.tensor([b0, b1, b2]) ) - assert torch.allclose(waveform_sox_out, waveform_lfilter_out, atol=1e-4) + torch.testing.assert_allclose(waveform_lfilter_out, waveform_sox_out, atol=1e-4, rtol=1e-5) if __name__ == "__main__": diff --git a/test/test_sox_effects.py b/test/test_sox_effects.py index 3445a5144b..487d73a53d 100644 --- a/test/test_sox_effects.py +++ b/test/test_sox_effects.py @@ -2,27 +2,17 @@ import torch import torchaudio import math -import os -from common_utils import AudioBackendScope, BACKENDS, create_temp_assets_dir +import common_utils +from common_utils import AudioBackendScope, BACKENDS @unittest.skipIf("sox" not in BACKENDS, "sox not available") class Test_SoxEffectsChain(unittest.TestCase): - test_dirpath, test_dir = create_temp_assets_dir() - test_filepath = os.path.join(test_dirpath, "assets", - "steam-train-whistle-daniel_simon.mp3") - - @classmethod - def setUpClass(cls): - torchaudio.initialize_sox() - - @classmethod - def tearDownClass(cls): - torchaudio.shutdown_sox() + test_filepath = common_utils.get_asset_path("steam-train-whistle-daniel_simon.mp3") def test_single_channel(self): - fn_sine = os.path.join(self.test_dirpath, "assets", "sinewave.wav") + fn_sine = common_utils.get_asset_path("sinewave.wav") E = torchaudio.sox_effects.SoxEffectsChain() E.set_input_file(fn_sine) E.append_effect_to_chain("echos", [0.8, 0.7, 40, 0.25, 63, 0.3]) @@ -259,6 +249,24 @@ def test_vol(self): # check if effect worked self.assertTrue(x.allclose(z, rtol=1e-4, atol=1e-4)) + def test_vad(self): + sample_files = [ + common_utils.get_asset_path("vad-hello-stereo-44100.wav"), + common_utils.get_asset_path("vad-hello-mono-32000.wav") + ] + + for sample_file in sample_files: + E = torchaudio.sox_effects.SoxEffectsChain() + E.set_input_file(sample_file) + E.append_effect_to_chain("vad") + x, _ = E.sox_build_flow_effects() + + x_orig, sample_rate = torchaudio.load(sample_file) + vad = torchaudio.transforms.Vad(sample_rate) + + y = vad(x_orig) + self.assertTrue(x.allclose(y, rtol=1e-4, atol=1e-4)) + if __name__ == '__main__': with AudioBackendScope("sox"): diff --git a/test/test_torchscript_consistency.py b/test/test_torchscript_consistency.py index fba009d29d..5f0b12c76b 100644 --- a/test/test_torchscript_consistency.py +++ b/test/test_torchscript_consistency.py @@ -1,413 +1,588 @@ """Test suites for jit-ability and its numerical compatibility""" -import os import unittest import torch import torchaudio import torchaudio.functional as F -import torchaudio.transforms +import torchaudio.transforms as T import common_utils -def _test_torchscript_functional_shape(py_method, *args, **kwargs): - jit_method = torch.jit.script(py_method) - - jit_out = jit_method(*args, **kwargs) - py_out = py_method(*args, **kwargs) - - assert jit_out.shape == py_out.shape - return jit_out, py_out - - -def _test_torchscript_functional(py_method, *args, **kwargs): - jit_out, py_out = _test_torchscript_functional_shape(py_method, *args, **kwargs) - assert torch.allclose(jit_out, py_out) - - -def _test_lfilter(waveform): - """ - Design an IIR lowpass filter using scipy.signal filter design - https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.iirdesign.html#scipy.signal.iirdesign - - Example - >>> from scipy.signal import iirdesign - >>> b, a = iirdesign(0.2, 0.3, 1, 60) - """ - b_coeffs = torch.tensor( - [ - 0.00299893, - -0.0051152, - 0.00841964, - -0.00747802, - 0.00841964, - -0.0051152, - 0.00299893, - ], - device=waveform.device, - ) - a_coeffs = torch.tensor( - [ - 1.0, - -4.8155751, - 10.2217618, - -12.14481273, - 8.49018171, - -3.3066882, - 0.56088705, - ], - device=waveform.device, - ) - _test_torchscript_functional(F.lfilter, waveform, a_coeffs, b_coeffs) - - -class TestFunctional(unittest.TestCase): - """Test functions in `functional` module.""" +def _assert_functional_consistency(func, tensor, device, shape_only=False): + tensor = tensor.to(device) + ts_func = torch.jit.script(func) + output = func(tensor) + ts_output = ts_func(tensor) + + if shape_only: + assert ts_output.shape == output.shape, (ts_output.shape, output.shape) + else: + torch.testing.assert_allclose(ts_output, output) + + +def _assert_transforms_consistency(transform, tensor, device): + tensor = tensor.to(device) + transform = transform.to(device) + ts_transform = torch.jit.script(transform) + output = transform(tensor) + ts_output = ts_transform(tensor) + torch.testing.assert_allclose(ts_output, output) + + +class _FunctionalTestMixin: + """Implements test for `functinoal` modul that are performed for different devices""" + device = None + + def _assert_consistency(self, func, tensor, shape_only=False): + return _assert_functional_consistency(func, tensor, self.device, shape_only=shape_only) + def test_spectrogram(self): + def func(tensor): + n_fft = 400 + ws = 400 + hop = 200 + pad = 0 + window = torch.hann_window(ws, device=tensor.device, dtype=tensor.dtype) + power = 2. + normalize = False + return F.spectrogram(tensor, pad, window, n_fft, hop, ws, power, normalize) + tensor = torch.rand((1, 1000)) - n_fft = 400 - ws = 400 - hop = 200 - pad = 0 - window = torch.hann_window(ws) - power = 2 - normalize = False - - _test_torchscript_functional( - F.spectrogram, tensor, pad, window, n_fft, hop, ws, power, normalize - ) + self._assert_consistency(func, tensor) def test_griffinlim(self): + def func(tensor): + n_fft = 400 + ws = 400 + hop = 200 + window = torch.hann_window(ws, device=tensor.device, dtype=tensor.dtype) + power = 2. + normalize = False + momentum = 0.99 + n_iter = 32 + length = 1000 + rand_int = False + return F.griffinlim(tensor, window, n_fft, hop, ws, power, normalize, n_iter, momentum, length, rand_int) + tensor = torch.rand((1, 201, 6)) - n_fft = 400 - ws = 400 - hop = 200 - window = torch.hann_window(ws) - power = 2 - normalize = False - momentum = 0.99 - n_iter = 32 - length = 1000 - - _test_torchscript_functional( - F.griffinlim, tensor, window, n_fft, hop, ws, power, normalize, n_iter, momentum, length, 0 - ) + self._assert_consistency(func, tensor) def test_compute_deltas(self): + def func(tensor): + win_length = 2 * 7 + 1 + return F.compute_deltas(tensor, win_length=win_length) + channel = 13 n_mfcc = channel * 3 time = 1021 - win_length = 2 * 7 + 1 - specgram = torch.randn(channel, n_mfcc, time) - - _test_torchscript_functional(F.compute_deltas, specgram, win_length=win_length) + tensor = torch.randn(channel, n_mfcc, time) + self._assert_consistency(func, tensor) def test_detect_pitch_frequency(self): - filepath = os.path.join( - common_utils.TEST_DIR_PATH, 'assets', 'steam-train-whistle-daniel_simon.mp3') - waveform, sample_rate = torchaudio.load(filepath) - _test_torchscript_functional(F.detect_pitch_frequency, waveform, sample_rate) + filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.mp3') + waveform, _ = torchaudio.load(filepath) + + def func(tensor): + sample_rate = 44100 + return F.detect_pitch_frequency(tensor, sample_rate) + + self._assert_consistency(func, waveform) def test_create_fb_matrix(self): - n_stft = 100 - f_min = 0.0 - f_max = 20.0 - n_mels = 10 - sample_rate = 16000 + if self.device != torch.device('cpu'): + raise unittest.SkipTest('No need to perform test on device other than CPU') + + def func(_): + n_stft = 100 + f_min = 0.0 + f_max = 20.0 + n_mels = 10 + sample_rate = 16000 + return F.create_fb_matrix(n_stft, f_min, f_max, n_mels, sample_rate) - _test_torchscript_functional(F.create_fb_matrix, n_stft, f_min, f_max, n_mels, sample_rate) + dummy = torch.zeros(1, 1) + self._assert_consistency(func, dummy) def test_amplitude_to_DB(self): - spec = torch.rand((6, 201)) - multiplier = 10.0 - amin = 1e-10 - db_multiplier = 0.0 - top_db = 80.0 + def func(tensor): + multiplier = 10.0 + amin = 1e-10 + db_multiplier = 0.0 + top_db = 80.0 + return F.amplitude_to_DB(tensor, multiplier, amin, db_multiplier, top_db) - _test_torchscript_functional(F.amplitude_to_DB, spec, multiplier, amin, db_multiplier, top_db) + tensor = torch.rand((6, 201)) + self._assert_consistency(func, tensor) def test_DB_to_amplitude(self): - x = torch.rand((1, 100)) - ref = 1. - power = 1. + def func(tensor): + ref = 1. + power = 1. + return F.DB_to_amplitude(tensor, ref, power) - _test_torchscript_functional(F.DB_to_amplitude, x, ref, power) + tensor = torch.rand((1, 100)) + self._assert_consistency(func, tensor) def test_create_dct(self): - n_mfcc = 40 - n_mels = 128 - norm = "ortho" + if self.device != torch.device('cpu'): + raise unittest.SkipTest('No need to perform test on device other than CPU') + + def func(_): + n_mfcc = 40 + n_mels = 128 + norm = "ortho" + return F.create_dct(n_mfcc, n_mels, norm) - _test_torchscript_functional(F.create_dct, n_mfcc, n_mels, norm) + dummy = torch.zeros(1, 1) + self._assert_consistency(func, dummy) def test_mu_law_encoding(self): - tensor = torch.rand((1, 10)) - qc = 256 + def func(tensor): + qc = 256 + return F.mu_law_encoding(tensor, qc) - _test_torchscript_functional(F.mu_law_encoding, tensor, qc) + tensor = torch.rand((1, 10)) + self._assert_consistency(func, tensor) def test_mu_law_decoding(self): - tensor = torch.rand((1, 10)) - qc = 256 + def func(tensor): + qc = 256 + return F.mu_law_decoding(tensor, qc) - _test_torchscript_functional(F.mu_law_decoding, tensor, qc) + tensor = torch.rand((1, 10)) + self._assert_consistency(func, tensor) def test_complex_norm(self): - complex_tensor = torch.randn(1, 2, 1025, 400, 2) - power = 2 + def func(tensor): + power = 2. + return F.complex_norm(tensor, power) - _test_torchscript_functional(F.complex_norm, complex_tensor, power) + tensor = torch.randn(1, 2, 1025, 400, 2) + _assert_functional_consistency(func, tensor, self.device) def test_mask_along_axis(self): - specgram = torch.randn(2, 1025, 400) - mask_param = 100 - mask_value = 30. - axis = 2 + def func(tensor): + mask_param = 100 + mask_value = 30. + axis = 2 + return F.mask_along_axis(tensor, mask_param, mask_value, axis) - _test_torchscript_functional(F.mask_along_axis, specgram, mask_param, mask_value, axis) + tensor = torch.randn(2, 1025, 400) + self._assert_consistency(func, tensor) def test_mask_along_axis_iid(self): - specgrams = torch.randn(4, 2, 1025, 400) - mask_param = 100 - mask_value = 30. - axis = 2 + def func(tensor): + mask_param = 100 + mask_value = 30. + axis = 2 + return F.mask_along_axis_iid(tensor, mask_param, mask_value, axis) - _test_torchscript_functional(F.mask_along_axis_iid, specgrams, mask_param, mask_value, axis) + tensor = torch.randn(4, 2, 1025, 400) + self._assert_consistency(func, tensor) def test_gain(self): + def func(tensor): + gainDB = 2.0 + return F.gain(tensor, gainDB) + tensor = torch.rand((1, 1000)) - gainDB = 2.0 + self._assert_consistency(func, tensor) - _test_torchscript_functional(F.gain, tensor, gainDB) + def test_dither_TPDF(self): + def func(tensor): + return F.dither(tensor, 'TPDF') - def test_dither(self): tensor = torch.rand((2, 1000)) + self._assert_consistency(func, tensor, shape_only=True) + + def test_dither_RPDF(self): + def func(tensor): + return F.dither(tensor, 'RPDF') + + tensor = torch.rand((2, 1000)) + self._assert_consistency(func, tensor, shape_only=True) + + def test_dither_GPDF(self): + def func(tensor): + return F.dither(tensor, 'GPDF') - _test_torchscript_functional_shape(F.dither, tensor) - _test_torchscript_functional_shape(F.dither, tensor, "RPDF") - _test_torchscript_functional_shape(F.dither, tensor, "GPDF") + tensor = torch.rand((2, 1000)) + self._assert_consistency(func, tensor, shape_only=True) def test_lfilter(self): - filepath = os.path.join(common_utils.TEST_DIR_PATH, 'assets', 'whitenoise.wav') + filepath = common_utils.get_asset_path('whitenoise.wav') waveform, _ = torchaudio.load(filepath, normalization=True) - _test_lfilter(waveform) - @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available") - def test_lfilter_cuda(self): - filepath = os.path.join(common_utils.TEST_DIR_PATH, "assets", "whitenoise.wav") - waveform, _ = torchaudio.load(filepath, normalization=True) - _test_lfilter(waveform.cuda(device=torch.device("cuda:0"))) + def func(tensor): + # Design an IIR lowpass filter using scipy.signal filter design + # https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.iirdesign.html#scipy.signal.iirdesign + # + # Example + # >>> from scipy.signal import iirdesign + # >>> b, a = iirdesign(0.2, 0.3, 1, 60) + b_coeffs = torch.tensor( + [ + 0.00299893, + -0.0051152, + 0.00841964, + -0.00747802, + 0.00841964, + -0.0051152, + 0.00299893, + ], + device=tensor.device, + dtype=tensor.dtype, + ) + a_coeffs = torch.tensor( + [ + 1.0, + -4.8155751, + 10.2217618, + -12.14481273, + 8.49018171, + -3.3066882, + 0.56088705, + ], + device=tensor.device, + dtype=tensor.dtype, + ) + return F.lfilter(tensor, a_coeffs, b_coeffs) + + self._assert_consistency(func, waveform) def test_lowpass(self): - cutoff_freq = 3000 + filepath = common_utils.get_asset_path('whitenoise.wav') + waveform, _ = torchaudio.load(filepath, normalization=True) - filepath = os.path.join(common_utils.TEST_DIR_PATH, 'assets', 'whitenoise.wav') - waveform, sample_rate = torchaudio.load(filepath, normalization=True) - _test_torchscript_functional(F.lowpass_biquad, waveform, sample_rate, cutoff_freq) + def func(tensor): + sample_rate = 44100 + cutoff_freq = 3000. + return F.lowpass_biquad(tensor, sample_rate, cutoff_freq) + + self._assert_consistency(func, waveform) def test_highpass(self): - cutoff_freq = 2000 + filepath = common_utils.get_asset_path('whitenoise.wav') + waveform, _ = torchaudio.load(filepath, normalization=True) - filepath = os.path.join(common_utils.TEST_DIR_PATH, 'assets', 'whitenoise.wav') - waveform, sample_rate = torchaudio.load(filepath, normalization=True) - _test_torchscript_functional(F.highpass_biquad, waveform, sample_rate, cutoff_freq) + def func(tensor): + sample_rate = 44100 + cutoff_freq = 2000. + return F.highpass_biquad(tensor, sample_rate, cutoff_freq) + + self._assert_consistency(func, waveform) def test_allpass(self): - central_freq = 1000 - q = 0.707 + filepath = common_utils.get_asset_path('whitenoise.wav') + waveform, _ = torchaudio.load(filepath, normalization=True) - filepath = os.path.join(common_utils.TEST_DIR_PATH, 'assets', 'whitenoise.wav') - waveform, sample_rate = torchaudio.load(filepath, normalization=True) - _test_torchscript_functional(F.allpass_biquad, waveform, sample_rate, central_freq, q) + def func(tensor): + sample_rate = 44100 + central_freq = 1000. + q = 0.707 + return F.allpass_biquad(tensor, sample_rate, central_freq, q) + + self._assert_consistency(func, waveform) def test_bandpass_with_csg(self): - central_freq = 1000 - q = 0.707 - const_skirt_gain = True + filepath = common_utils.get_asset_path("whitenoise.wav") + waveform, _ = torchaudio.load(filepath, normalization=True) - filepath = os.path.join(common_utils.TEST_DIR_PATH, "assets", "whitenoise.wav") - waveform, sample_rate = torchaudio.load(filepath, normalization=True) - _test_torchscript_functional( - F.bandpass_biquad, waveform, sample_rate, central_freq, q, const_skirt_gain) + def func(tensor): + sample_rate = 44100 + central_freq = 1000. + q = 0.707 + const_skirt_gain = True + return F.bandpass_biquad(tensor, sample_rate, central_freq, q, const_skirt_gain) + + self._assert_consistency(func, waveform) def test_bandpass_withou_csg(self): - central_freq = 1000 - q = 0.707 - const_skirt_gain = False + filepath = common_utils.get_asset_path("whitenoise.wav") + waveform, _ = torchaudio.load(filepath, normalization=True) + + def func(tensor): + sample_rate = 44100 + central_freq = 1000. + q = 0.707 + const_skirt_gain = True + return F.bandpass_biquad(tensor, sample_rate, central_freq, q, const_skirt_gain) - filepath = os.path.join(common_utils.TEST_DIR_PATH, "assets", "whitenoise.wav") - waveform, sample_rate = torchaudio.load(filepath, normalization=True) - _test_torchscript_functional( - F.bandpass_biquad, waveform, sample_rate, central_freq, q, const_skirt_gain) + self._assert_consistency(func, waveform) def test_bandreject(self): - central_freq = 1000 - q = 0.707 + filepath = common_utils.get_asset_path("whitenoise.wav") + waveform, _ = torchaudio.load(filepath, normalization=True) + + def func(tensor): + sample_rate = 44100 + central_freq = 1000. + q = 0.707 + return F.bandreject_biquad(tensor, sample_rate, central_freq, q) - filepath = os.path.join(common_utils.TEST_DIR_PATH, "assets", "whitenoise.wav") - waveform, sample_rate = torchaudio.load(filepath, normalization=True) - _test_torchscript_functional( - F.bandreject_biquad, waveform, sample_rate, central_freq, q) + self._assert_consistency(func, waveform) def test_band_with_noise(self): - central_freq = 1000 - q = 0.707 - noise = True + filepath = common_utils.get_asset_path("whitenoise.wav") + waveform, _ = torchaudio.load(filepath, normalization=True) + + def func(tensor): + sample_rate = 44100 + central_freq = 1000. + q = 0.707 + noise = True + return F.band_biquad(tensor, sample_rate, central_freq, q, noise) - filepath = os.path.join(common_utils.TEST_DIR_PATH, "assets", "whitenoise.wav") - waveform, sample_rate = torchaudio.load(filepath, normalization=True) - _test_torchscript_functional(F.band_biquad, waveform, sample_rate, central_freq, q, noise) + self._assert_consistency(func, waveform) def test_band_without_noise(self): - central_freq = 1000 - q = 0.707 - noise = False + filepath = common_utils.get_asset_path("whitenoise.wav") + waveform, _ = torchaudio.load(filepath, normalization=True) - filepath = os.path.join(common_utils.TEST_DIR_PATH, "assets", "whitenoise.wav") - waveform, sample_rate = torchaudio.load(filepath, normalization=True) - _test_torchscript_functional(F.band_biquad, waveform, sample_rate, central_freq, q, noise) + def func(tensor): + sample_rate = 44100 + central_freq = 1000. + q = 0.707 + noise = False + return F.band_biquad(tensor, sample_rate, central_freq, q, noise) + + self._assert_consistency(func, waveform) def test_treble(self): - gain = 40 - central_freq = 1000 - q = 0.707 + filepath = common_utils.get_asset_path("whitenoise.wav") + waveform, _ = torchaudio.load(filepath, normalization=True) - filepath = os.path.join(common_utils.TEST_DIR_PATH, "assets", "whitenoise.wav") - waveform, sample_rate = torchaudio.load(filepath, normalization=True) - _test_torchscript_functional(F.treble_biquad, waveform, sample_rate, gain, central_freq, q) + def func(tensor): + sample_rate = 44100 + gain = 40. + central_freq = 1000. + q = 0.707 + return F.treble_biquad(tensor, sample_rate, gain, central_freq, q) + + self._assert_consistency(func, waveform) def test_deemph(self): - filepath = os.path.join(common_utils.TEST_DIR_PATH, "assets", "whitenoise.wav") - waveform, sample_rate = torchaudio.load(filepath, normalization=True) - _test_torchscript_functional(F.deemph_biquad, waveform, sample_rate) + filepath = common_utils.get_asset_path("whitenoise.wav") + waveform, _ = torchaudio.load(filepath, normalization=True) + + def func(tensor): + sample_rate = 44100 + return F.deemph_biquad(tensor, sample_rate) + + self._assert_consistency(func, waveform) def test_riaa(self): - filepath = os.path.join(common_utils.TEST_DIR_PATH, "assets", "whitenoise.wav") - waveform, sample_rate = torchaudio.load(filepath, normalization=True) - _test_torchscript_functional(F.riaa_biquad, waveform, sample_rate) + filepath = common_utils.get_asset_path("whitenoise.wav") + waveform, _ = torchaudio.load(filepath, normalization=True) + + def func(tensor): + sample_rate = 44100 + return F.riaa_biquad(tensor, sample_rate) + + self._assert_consistency(func, waveform) def test_equalizer(self): - center_freq = 300 - gain = 1 - q = 0.707 + filepath = common_utils.get_asset_path("whitenoise.wav") + waveform, _ = torchaudio.load(filepath, normalization=True) + + def func(tensor): + sample_rate = 44100 + center_freq = 300. + gain = 1. + q = 0.707 + return F.equalizer_biquad(tensor, sample_rate, center_freq, gain, q) - filepath = os.path.join(common_utils.TEST_DIR_PATH, "assets", "whitenoise.wav") - waveform, sample_rate = torchaudio.load(filepath, normalization=True) - _test_torchscript_functional( - F.equalizer_biquad, waveform, sample_rate, center_freq, gain, q) + self._assert_consistency(func, waveform) def test_perf_biquad_filtering(self): - a = torch.tensor([0.7, 0.2, 0.6]) - b = torch.tensor([0.4, 0.2, 0.9]) - filepath = os.path.join(common_utils.TEST_DIR_PATH, "assets", "whitenoise.wav") + filepath = common_utils.get_asset_path("whitenoise.wav") waveform, _ = torchaudio.load(filepath, normalization=True) - _test_torchscript_functional(F.lfilter, waveform, a, b) + def func(tensor): + a = torch.tensor([0.7, 0.2, 0.6], device=tensor.device, dtype=tensor.dtype) + b = torch.tensor([0.4, 0.2, 0.9], device=tensor.device, dtype=tensor.dtype) + return F.lfilter(tensor, a, b) + + self._assert_consistency(func, waveform) + + def test_sliding_window_cmn(self): + def func(tensor): + cmn_window = 600 + min_cmn_window = 100 + center = False + norm_vars = False + a = torch.tensor( + [ + [ + -1.915875792503357, + 1.147700309753418 + ], + [ + 1.8242558240890503, + 1.3869990110397339 + ] + ], + device=tensor.device, + dtype=tensor.dtype + ) + return F.sliding_window_cmn(a, cmn_window, min_cmn_window, center, norm_vars) + b = torch.tensor( + [ + [ + -1.8701, + -0.1196 + ], + [ + 1.8701, + 0.1196 + ] + ] + ) + self._assert_consistency(func, b) -RUN_CUDA = torch.cuda.is_available() -print("Run test with cuda:", RUN_CUDA) + def test_contrast(self): + filepath = common_utils.get_asset_path("whitenoise.wav") + waveform, _ = torchaudio.load(filepath, normalization=True) + def func(tensor): + enhancement_amount = 80. + return F.contrast(tensor, enhancement_amount) -def _test_script_module(f, tensor, *args, **kwargs): + self._assert_consistency(func, waveform) - py_method = f(*args, **kwargs) - jit_method = torch.jit.script(py_method) + def test_dcshift(self): + filepath = common_utils.get_asset_path("whitenoise.wav") + waveform, _ = torchaudio.load(filepath, normalization=True) - py_out = py_method(tensor) - jit_out = jit_method(tensor) + def func(tensor): + shift = 0.5 + limiter_gain = 0.05 + return F.dcshift(tensor, shift, limiter_gain) - assert torch.allclose(jit_out, py_out) + self._assert_consistency(func, waveform) - if RUN_CUDA: + def test_overdrive(self): + filepath = common_utils.get_asset_path("whitenoise.wav") + waveform, _ = torchaudio.load(filepath, normalization=True) - tensor = tensor.to("cuda") + def func(tensor): + gain = 30. + colour = 50. + return F.overdrive(tensor, gain, colour) - py_method = py_method.cuda() - jit_method = torch.jit.script(py_method) + self._assert_consistency(func, waveform) - py_out = py_method(tensor) - jit_out = jit_method(tensor) - assert torch.allclose(jit_out, py_out) +class _TransformsTestMixin: + """Implements test for Transforms that are performed for different devices""" + device = None + def _assert_consistency(self, transform, tensor): + _assert_transforms_consistency(transform, tensor, self.device) -class TestTransforms(unittest.TestCase): def test_Spectrogram(self): tensor = torch.rand((1, 1000)) - _test_script_module(torchaudio.transforms.Spectrogram, tensor) + self._assert_consistency(T.Spectrogram(), tensor) def test_GriffinLim(self): tensor = torch.rand((1, 201, 6)) - _test_script_module(torchaudio.transforms.GriffinLim, tensor, length=1000, rand_init=False) + self._assert_consistency(T.GriffinLim(length=1000, rand_init=False), tensor) def test_AmplitudeToDB(self): spec = torch.rand((6, 201)) - _test_script_module(torchaudio.transforms.AmplitudeToDB, spec) + self._assert_consistency(T.AmplitudeToDB(), spec) def test_MelScale(self): spec_f = torch.rand((1, 6, 201)) - _test_script_module(torchaudio.transforms.MelScale, spec_f) + self._assert_consistency(T.MelScale(), spec_f) def test_MelSpectrogram(self): tensor = torch.rand((1, 1000)) - _test_script_module(torchaudio.transforms.MelSpectrogram, tensor) + self._assert_consistency(T.MelSpectrogram(), tensor) def test_MFCC(self): tensor = torch.rand((1, 1000)) - _test_script_module(torchaudio.transforms.MFCC, tensor) + self._assert_consistency(T.MFCC(), tensor) def test_Resample(self): tensor = torch.rand((2, 1000)) sample_rate = 100. sample_rate_2 = 50. - - _test_script_module(torchaudio.transforms.Resample, tensor, sample_rate, sample_rate_2) + self._assert_consistency(T.Resample(sample_rate, sample_rate_2), tensor) def test_ComplexNorm(self): tensor = torch.rand((1, 2, 201, 2)) - _test_script_module(torchaudio.transforms.ComplexNorm, tensor) + self._assert_consistency(T.ComplexNorm(), tensor) def test_MuLawEncoding(self): tensor = torch.rand((1, 10)) - _test_script_module(torchaudio.transforms.MuLawEncoding, tensor) + self._assert_consistency(T.MuLawEncoding(), tensor) def test_MuLawDecoding(self): tensor = torch.rand((1, 10)) - _test_script_module(torchaudio.transforms.MuLawDecoding, tensor) + self._assert_consistency(T.MuLawDecoding(), tensor) def test_TimeStretch(self): n_freq = 400 hop_length = 512 fixed_rate = 1.3 tensor = torch.rand((10, 2, n_freq, 10, 2)) - _test_script_module( - torchaudio.transforms.TimeStretch, - tensor, n_freq=n_freq, hop_length=hop_length, fixed_rate=fixed_rate) + self._assert_consistency( + T.TimeStretch(n_freq=n_freq, hop_length=hop_length, fixed_rate=fixed_rate), + tensor, + ) def test_Fade(self): - test_filepath = os.path.join( - common_utils.TEST_DIR_PATH, 'assets', 'steam-train-whistle-daniel_simon.wav') + test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') waveform, _ = torchaudio.load(test_filepath) fade_in_len = 3000 fade_out_len = 3000 - - _test_script_module(torchaudio.transforms.Fade, waveform, fade_in_len, fade_out_len) + self._assert_consistency(T.Fade(fade_in_len, fade_out_len), waveform) def test_FrequencyMasking(self): tensor = torch.rand((10, 2, 50, 10, 2)) - _test_script_module( - torchaudio.transforms.FrequencyMasking, tensor, freq_mask_param=60, iid_masks=False) + self._assert_consistency(T.FrequencyMasking(freq_mask_param=60, iid_masks=False), tensor) def test_TimeMasking(self): tensor = torch.rand((10, 2, 50, 10, 2)) - _test_script_module( - torchaudio.transforms.TimeMasking, tensor, time_mask_param=30, iid_masks=False) + self._assert_consistency(T.TimeMasking(time_mask_param=30, iid_masks=False), tensor) def test_Vol(self): - test_filepath = os.path.join( - common_utils.TEST_DIR_PATH, 'assets', 'steam-train-whistle-daniel_simon.wav') + test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') waveform, _ = torchaudio.load(test_filepath) - _test_script_module(torchaudio.transforms.Vol, waveform, 1.1) + self._assert_consistency(T.Vol(1.1), waveform) + + def test_SlidingWindowCmn(self): + tensor = torch.rand((1000, 10)) + self._assert_consistency(T.SlidingWindowCmn(), tensor) + + def test_Vad(self): + filepath = common_utils.get_asset_path("vad-hello-mono-32000.wav") + waveform, sample_rate = torchaudio.load(filepath) + self._assert_consistency(T.Vad(sample_rate=sample_rate), waveform) + + +class TestFunctionalCPU(_FunctionalTestMixin, unittest.TestCase): + """Test suite for Functional module on CPU""" + device = torch.device('cpu') + + +@unittest.skipIf(not torch.cuda.is_available(), 'CUDA not available') +class TestFunctionalCUDA(_FunctionalTestMixin, unittest.TestCase): + """Test suite for Functional module on GPU""" + device = torch.device('cuda') + + +class TestTransformsCPU(_TransformsTestMixin, unittest.TestCase): + """Test suite for Transforms module on CPU""" + device = torch.device('cpu') + + +@unittest.skipIf(not torch.cuda.is_available(), 'CUDA not available') +class TestTransformsCUDA(_TransformsTestMixin, unittest.TestCase): + """Test suite for Transforms module on GPU""" + device = torch.device('cuda') if __name__ == '__main__': diff --git a/test/test_transforms.py b/test/test_transforms.py index f8501e72e2..9c0313ba7c 100644 --- a/test/test_transforms.py +++ b/test/test_transforms.py @@ -1,5 +1,4 @@ import math -import os import unittest import torch @@ -7,7 +6,7 @@ import torchaudio.transforms as transforms import torchaudio.functional as F -from common_utils import AudioBackendScope, BACKENDS, create_temp_assets_dir +import common_utils class Tester(unittest.TestCase): @@ -19,10 +18,6 @@ class Tester(unittest.TestCase): waveform = (torch.cos(2 * math.pi * torch.arange(0, 4 * sample_rate).float() * freq / sample_rate)) waveform.unsqueeze_(0) # (1, 64000) waveform = (waveform * volume * 2**31).long() - # file for stereo stft test - test_dirpath, test_dir = create_temp_assets_dir() - test_filepath = os.path.join(test_dirpath, 'assets', - 'steam-train-whistle-daniel_simon.wav') def scale(self, waveform, factor=2.0**31): # scales a waveform by a factor @@ -45,7 +40,8 @@ def test_mu_law_companding(self): self.assertTrue(waveform_exp.min() >= -1. and waveform_exp.max() <= 1.) def test_AmplitudeToDB(self): - waveform, sample_rate = torchaudio.load(self.test_filepath) + filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') + waveform, sample_rate = torchaudio.load(filepath) mag_to_db_transform = transforms.AmplitudeToDB('magnitude', 80.) power_to_db_transform = transforms.AmplitudeToDB('power', 80.) @@ -53,7 +49,7 @@ def test_AmplitudeToDB(self): mag_to_db_torch = mag_to_db_transform(torch.abs(waveform)) power_to_db_torch = power_to_db_transform(torch.pow(waveform, 2)) - self.assertTrue(torch.allclose(mag_to_db_torch, power_to_db_torch)) + torch.testing.assert_allclose(mag_to_db_torch, power_to_db_torch) def test_melscale_load_save(self): specgram = torch.ones(1, 1000, 100) @@ -67,7 +63,7 @@ def test_melscale_load_save(self): fb_copy = melscale_transform_copy.fb self.assertEqual(fb_copy.size(), (1000, 128)) - self.assertTrue(torch.allclose(fb, fb_copy)) + torch.testing.assert_allclose(fb, fb_copy) def test_melspectrogram_load_save(self): waveform = self.waveform.float() @@ -83,10 +79,10 @@ def test_melspectrogram_load_save(self): fb = mel_spectrogram_transform.mel_scale.fb fb_copy = mel_spectrogram_transform_copy.mel_scale.fb - self.assertTrue(torch.allclose(window, window_copy)) + torch.testing.assert_allclose(window, window_copy) # the default for n_fft = 400 and n_mels = 128 self.assertEqual(fb_copy.size(), (201, 128)) - self.assertTrue(torch.allclose(fb, fb_copy)) + torch.testing.assert_allclose(fb, fb_copy) def test_mel2(self): top_db = 80. @@ -114,7 +110,8 @@ def test_mel2(self): self.assertTrue(mel_transform2.mel_scale.fb.sum(1).le(1.).all()) self.assertTrue(mel_transform2.mel_scale.fb.sum(1).ge(0.).all()) # check on multi-channel audio - x_stereo, sr_stereo = torchaudio.load(self.test_filepath) # (2, 278756), 44100 + filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') + x_stereo, sr_stereo = torchaudio.load(filepath) # (2, 278756), 44100 spectrogram_stereo = s2db(mel_transform(x_stereo)) # (2, 128, 1394) self.assertTrue(spectrogram_stereo.dim() == 3) self.assertTrue(spectrogram_stereo.size(0) == 2) @@ -164,7 +161,7 @@ def test_mfcc(self): self.assertTrue(torch_mfcc_norm_none.allclose(norm_check)) def test_resample_size(self): - input_path = os.path.join(self.test_dirpath, 'assets', 'sinewave.wav') + input_path = common_utils.get_asset_path('sinewave.wav') waveform, sample_rate = torchaudio.load(input_path) upsample_rate = sample_rate * 2 @@ -217,7 +214,7 @@ def test_compute_deltas_twochannel(self): transform = transforms.ComputeDeltas(win_length=3) computed = transform(specgram) assert computed.shape == expected.shape, (computed.shape, expected.shape) - assert torch.allclose(computed, expected, atol=1e-6, rtol=1e-8) + torch.testing.assert_allclose(computed, expected, atol=1e-6, rtol=1e-8) if __name__ == '__main__': diff --git a/torchaudio/__init__.py b/torchaudio/__init__.py index 2b04a2f28a..e748a34bc2 100644 --- a/torchaudio/__init__.py +++ b/torchaudio/__init__.py @@ -1,15 +1,17 @@ +import atexit import os.path +from pathlib import Path +from typing import Any, Callable, Optional, Tuple, Union import torch - +from torch import Tensor from torchaudio import ( compliance, datasets, kaldi_io, sox_effects, - transforms, + transforms ) - from torchaudio._backend import ( check_input, _audio_backend_guard, @@ -17,6 +19,7 @@ get_audio_backend, set_audio_backend, ) +from torchaudio._soundfile_backend import SignalInfo, EncodingInfo try: from .version import __version__, git_version # noqa: F401 @@ -24,27 +27,27 @@ pass -def load(filepath, - out=None, - normalization=True, - channels_first=True, - num_frames=0, - offset=0, - signalinfo=None, - encodinginfo=None, - filetype=None): +def load(filepath: Union[str, Path], + out: Optional[Tensor] = None, + normalization: Union[bool, float, Callable] = True, + channels_first: bool = True, + num_frames: int = 0, + offset: int = 0, + signalinfo: Optional[SignalInfo] = None, + encodinginfo: Optional[EncodingInfo] = None, + filetype: Optional[str] = None) -> Tuple[Tensor, int]: r"""Loads an audio file from disk into a tensor Args: - filepath (str or pathlib.Path): Path to audio file - out (torch.Tensor, optional): An output tensor to use instead of creating one. (Default: ``None``) - normalization (bool, number, or callable, optional): If boolean `True`, then output is divided by `1 << 31` + filepath (str or Path): Path to audio file + out (Tensor, optional): An output tensor to use instead of creating one. (Default: ``None``) + normalization (bool, float, or callable, optional): If boolean `True`, then output is divided by `1 << 31` (assumes signed 32-bit audio), and normalizes to `[-1, 1]`. - If `number`, then output is divided by that number - If `callable`, then the output is passed as a parameter + If `float`, then output is divided by that number + If `Callable`, then the output is passed as a parameter to the given function, then the output is divided by the result. (Default: ``True``) - channels_first (bool): Set channels first or length first in result. (Default: ``True``) + channels_first (bool, optional): Set channels first or length first in result. (Default: ``True``) num_frames (int, optional): Number of frames to load. 0 to load everything after the offset. (Default: ``0``) offset (int, optional): Number of frames from the start of the file to begin data loading. @@ -57,7 +60,7 @@ def load(filepath, automatically. (Default: ``None``) Returns: - Tuple[torch.Tensor, int]: An output tensor of size `[C x L]` or `[L x C]` where L is the number + (Tensor, int): An output tensor of size `[C x L]` or `[L x C]` where L is the number of audio frames and C is the number of channels. An integer which is the sample rate of the audio (as listed in the metadata of the file) @@ -86,15 +89,15 @@ def load(filepath, ) -def load_wav(filepath, **kwargs): +def load_wav(filepath: Union[str, Path], **kwargs: Any) -> Tuple[Tensor, int]: r""" Loads a wave file. It assumes that the wav file uses 16 bit per sample that needs normalization by shifting the input right by 16 bits. Args: - filepath (str or pathlib.Path): Path to audio file + filepath (str or Path): Path to audio file Returns: - Tuple[torch.Tensor, int]: An output tensor of size `[C x L]` or `[L x C]` where L is the number + (Tensor, int): An output tensor of size `[C x L]` or `[L x C]` where L is the number of audio frames and C is the number of channels. An integer which is the sample rate of the audio (as listed in the metadata of the file) """ @@ -102,17 +105,17 @@ def load_wav(filepath, **kwargs): return load(filepath, **kwargs) -def save(filepath, src, sample_rate, precision=16, channels_first=True): +def save(filepath: str, src: Tensor, sample_rate: int, precision: int = 16, channels_first: bool = True) -> None: r"""Convenience function for `save_encinfo`. Args: filepath (str): Path to audio file - src (torch.Tensor): An input 2D tensor of shape `[C x L]` or `[L x C]` where L is + src (Tensor): An input 2D tensor of shape `[C x L]` or `[L x C]` where L is the number of audio frames, C is the number of channels sample_rate (int): An integer which is the sample rate of the audio (as listed in the metadata of the file) - precision (int): Bit precision (Default: ``16``) - channels_first (bool): Set channels first or length first in result. ( + precision (int, optional): Bit precision (Default: ``16``) + channels_first (bool, optional): Set channels first or length first in result. ( Default: ``True``) """ @@ -122,23 +125,23 @@ def save(filepath, src, sample_rate, precision=16, channels_first=True): @_audio_backend_guard("sox") -def save_encinfo(filepath, - src, - channels_first=True, - signalinfo=None, - encodinginfo=None, - filetype=None): +def save_encinfo(filepath: str, + src: Tensor, + channels_first: bool = True, + signalinfo: Optional[SignalInfo] = None, + encodinginfo: Optional[EncodingInfo] = None, + filetype: Optional[str] = None) -> None: r"""Saves a tensor of an audio signal to disk as a standard format like mp3, wav, etc. Args: filepath (str): Path to audio file - src (torch.Tensor): An input 2D tensor of shape `[C x L]` or `[L x C]` where L is + src (Tensor): An input 2D tensor of shape `[C x L]` or `[L x C]` where L is the number of audio frames, C is the number of channels - channels_first (bool): Set channels first or length first in result. (Default: ``True``) - signalinfo (sox_signalinfo_t): A sox_signalinfo_t type, which could be helpful if the - audio type cannot be automatically determined. (Default: ``None``) + channels_first (bool, optional): Set channels first or length first in result. (Default: ``True``) + signalinfo (sox_signalinfo_t, optional): A sox_signalinfo_t type, which could be helpful if the + audio type cannot be automatically determined (Default: ``None``). encodinginfo (sox_encodinginfo_t, optional): A sox_encodinginfo_t type, which could be set if the - audio type cannot be automatically determined. (Default: ``None``) + audio type cannot be automatically determined (Default: ``None``). filetype (str, optional): A filetype or extension to be set if sox cannot determine it automatically. (Default: ``None``) @@ -165,17 +168,18 @@ def save_encinfo(filepath, "Expected format where C < 16, but found {}".format(src.size())) # sox stores the sample rate as a float, though practically sample rates are almost always integers # convert integers to floats - if not isinstance(signalinfo.rate, float): - if float(signalinfo.rate) == signalinfo.rate: - signalinfo.rate = float(signalinfo.rate) - else: - raise TypeError('Sample rate should be a float or int') - # check if the bit precision (i.e. bits per sample) is an integer - if not isinstance(signalinfo.precision, int): - if int(signalinfo.precision) == signalinfo.precision: - signalinfo.precision = int(signalinfo.precision) - else: - raise TypeError('Bit precision should be an integer') + if signalinfo: + if signalinfo.rate and not isinstance(signalinfo.rate, float): + if float(signalinfo.rate) == signalinfo.rate: + signalinfo.rate = float(signalinfo.rate) + else: + raise TypeError('Sample rate should be a float or int') + # check if the bit precision (i.e. bits per sample) is an integer + if signalinfo.precision and not isinstance(signalinfo.precision, int): + if int(signalinfo.precision) == signalinfo.precision: + signalinfo.precision = int(signalinfo.precision) + else: + raise TypeError('Bit precision should be an integer') # programs such as librosa normalize the signal, unnormalize if detected if src.min() >= -1.0 and src.max() <= 1.0: src = src * (1 << 31) @@ -193,14 +197,14 @@ def save_encinfo(filepath, _torch_sox.write_audio_file(filepath, src, signalinfo, encodinginfo, filetype) -def info(filepath): +def info(filepath: str) -> Tuple[SignalInfo, EncodingInfo]: r"""Gets metadata from an audio file without loading the signal. Args: filepath (str): Path to audio file Returns: - Tuple[sox_signalinfo_t, sox_encodinginfo_t]: A si (sox_signalinfo_t) signal + (sox_signalinfo_t, sox_encodinginfo_t): A si (sox_signalinfo_t) signal info as a python object. An ei (sox_encodinginfo_t) encoding info Example @@ -212,7 +216,7 @@ def info(filepath): @_audio_backend_guard("sox") -def sox_signalinfo_t(): +def sox_signalinfo_t() -> SignalInfo: r"""Create a sox_signalinfo_t object. This object can be used to set the sample rate, number of channels, length, bit precision and headroom multiplier primarily for effects @@ -237,7 +241,7 @@ def sox_signalinfo_t(): @_audio_backend_guard("sox") -def sox_encodinginfo_t(): +def sox_encodinginfo_t() -> EncodingInfo: r"""Create a sox_encodinginfo_t object. This object can be used to set the encoding type, bit precision, compression factor, reverse bytes, reverse nibbles, reverse bits and endianness. This can be used in an effects chain to encode the @@ -277,7 +281,7 @@ def sox_encodinginfo_t(): @_audio_backend_guard("sox") -def get_sox_encoding_t(i=None): +def get_sox_encoding_t(i: int = None) -> EncodingInfo: r"""Get enum of sox_encoding_t for sox encodings. Args: @@ -297,7 +301,7 @@ def get_sox_encoding_t(i=None): @_audio_backend_guard("sox") -def get_sox_option_t(i=2): +def get_sox_option_t(i: int = 2) -> Any: r"""Get enum of sox_option_t for sox encodinginfo options. Args: @@ -316,7 +320,7 @@ def get_sox_option_t(i=2): @_audio_backend_guard("sox") -def get_sox_bool(i=0): +def get_sox_bool(i: int = 0) -> Any: r"""Get enum of sox_bool for sox encodinginfo options. Args: @@ -335,28 +339,70 @@ def get_sox_bool(i=0): return _torch_sox.sox_bool(i) -@_audio_backend_guard("sox") -def initialize_sox(): - """Initialize sox for use with effects chains. This is not required for simple - loading. Importantly, only run `initialize_sox` once and do not shutdown - after each effect chain, but rather once you are finished with all effects chains. - """ +_SOX_INITIALIZED = False +# This variable has a micro lifecycle. (False -> True -> None) +# False: Not initialized +# True: Initialized +# None: Already shut down (should not be initialized again.) - import _torch_sox - return _torch_sox.initialize_sox() +_SOX_SUCCESS_CODE = 0 +# defined at +# https://fossies.org/dox/sox-14.4.2/sox_8h.html#a8e07e80cebeff3339265d89c387cea93a9ef2b87ec303edfe40751d9a85fadeeb @_audio_backend_guard("sox") -def shutdown_sox(): - """Showdown sox for effects chain. Not required for simple loading. Importantly, - only call once. Attempting to re-initialize sox will result in seg faults. +def initialize_sox() -> int: + """Initialize sox for use with effects chains. + + You only need to call this function once to use SoX effects chains multiple times. + It is safe to call this function multiple times as long as ``shutdown_sox`` is not yet called. + Once ``shutdown_sox`` is called, you can no longer use SoX effects and calling this function + results in `RuntimeError`. + + Note: + This function is not required for simple loading. + + Returns: + int: Code corresponding to sox_error_t enum. See + https://fossies.org/dox/sox-14.4.2/sox_8h.html#a8e07e80cebeff3339265d89c387cea93 """ + global _SOX_INITIALIZED + if _SOX_INITIALIZED is None: + raise RuntimeError('SoX effects chain has been already shut down. Can not initialize again.') + if not _SOX_INITIALIZED: + import _torch_sox + code = _torch_sox.initialize_sox() + if code == _SOX_SUCCESS_CODE: + _SOX_INITIALIZED = True + atexit.register(shutdown_sox) + return code + return _SOX_SUCCESS_CODE - import _torch_sox - return _torch_sox.shutdown_sox() + +@_audio_backend_guard("sox") +def shutdown_sox() -> int: + """Showdown sox for effects chain. + + You do not need to call this function as it will be called automatically + at the end of program execution, if ``initialize_sox`` was called. + + It is safe to call this function multiple times. + + Returns: + int: Code corresponding to sox_error_t enum. See + https://fossies.org/dox/sox-14.4.2/sox_8h.html#a8e07e80cebeff3339265d89c387cea93 + """ + global _SOX_INITIALIZED + if _SOX_INITIALIZED: + import _torch_sox + code = _torch_sox.shutdown_sox() + if code == _SOX_INITIALIZED: + _SOX_INITIALIZED = None + return code + return _SOX_SUCCESS_CODE -def _audio_normalization(signal, normalization): +def _audio_normalization(signal: Tensor, normalization: Union[bool, float, Callable]) -> None: """Audio normalization of a tensor in-place. The normalization can be a bool, a number, or a callable that takes the audio tensor as an input. SoX uses 32-bit signed integers internally, thus bool normalizes based on that assumption. diff --git a/torchaudio/_backend.py b/torchaudio/_backend.py index d95b8159ab..f5646d7970 100644 --- a/torchaudio/_backend.py +++ b/torchaudio/_backend.py @@ -1,20 +1,26 @@ from functools import wraps +from typing import Any, List, Union import platform import torch +from torch import Tensor from . import _soundfile_backend, _sox_backend -_audio_backend = "soundfile" if platform.system() == "Windows" else "sox" -_audio_backends = {"sox": _sox_backend, "soundfile": _soundfile_backend} +if platform.system() == "Windows": + _audio_backend = "soundfile" + _audio_backends = {"soundfile": _soundfile_backend} +else: + _audio_backend = "sox" + _audio_backends = {"sox": _sox_backend, "soundfile": _soundfile_backend} -def set_audio_backend(backend): +def set_audio_backend(backend: str) -> None: """ Specifies the package used to load. Args: - backend (string): Name of the backend. One of {}. + backend (str): Name of the backend. One of {}. """.format(_audio_backends.keys()) global _audio_backend if backend not in _audio_backends: @@ -24,14 +30,14 @@ def set_audio_backend(backend): _audio_backend = backend -def get_audio_backend(): +def get_audio_backend() -> str: """ Gets the name of the package used to load. """ return _audio_backend -def _get_audio_backend_module(): +def _get_audio_backend_module() -> Any: """ Gets the module backend to load. """ @@ -39,7 +45,7 @@ def _get_audio_backend_module(): return _audio_backends[backend] -def _audio_backend_guard(backends): +def _audio_backend_guard(backends: Union[str, List[str]]) -> Any: if isinstance(backends, str): backends = [backends] @@ -55,7 +61,7 @@ def wrapper(*args, **kwargs): return decorator -def check_input(src): +def check_input(src: Tensor) -> None: if not torch.is_tensor(src): raise TypeError('Expected a tensor, got %s' % type(src)) if src.is_cuda: diff --git a/torchaudio/_soundfile_backend.py b/torchaudio/_soundfile_backend.py index f3b72d071a..1a88ba5e80 100644 --- a/torchaudio/_soundfile_backend.py +++ b/torchaudio/_soundfile_backend.py @@ -1,6 +1,8 @@ import os +from typing import Any, Optional, Tuple, Union import torch +from torch import Tensor _subtype_to_precision = { 'PCM_S8': 8, @@ -12,7 +14,11 @@ class SignalInfo: - def __init__(self, channels=None, rate=None, precision=None, length=None): + def __init__(self, + channels: Optional[int] = None, + rate: Optional[float] = None, + precision: Optional[int] = None, + length: Optional[int] = None) -> None: self.channels = channels self.rate = rate self.precision = precision @@ -20,16 +26,14 @@ def __init__(self, channels=None, rate=None, precision=None, length=None): class EncodingInfo: - def __init__( - self, - encoding=None, - bits_per_sample=None, - compression=None, - reverse_bytes=None, - reverse_nibbles=None, - reverse_bits=None, - opposite_endian=None - ): + def __init__(self, + encoding: Any = None, + bits_per_sample: Optional[int] = None, + compression: Optional[float] = None, + reverse_bytes: Any = None, + reverse_nibbles: Any = None, + reverse_bits: Any = None, + opposite_endian: Optional[bool] = None) -> None: self.encoding = encoding self.bits_per_sample = bits_per_sample self.compression = compression @@ -39,24 +43,22 @@ def __init__( self.opposite_endian = opposite_endian -def check_input(src): +def check_input(src: Tensor) -> None: if not torch.is_tensor(src): raise TypeError("Expected a tensor, got %s" % type(src)) if src.is_cuda: raise TypeError("Expected a CPU based tensor, got %s" % type(src)) -def load( - filepath, - out=None, - normalization=True, - channels_first=True, - num_frames=0, - offset=0, - signalinfo=None, - encodinginfo=None, - filetype=None, -): +def load(filepath: str, + out: Optional[Tensor] = None, + normalization: Optional[bool] = True, + channels_first: Optional[bool] = True, + num_frames: int = 0, + offset: int = 0, + signalinfo: SignalInfo = None, + encodinginfo: EncodingInfo = None, + filetype: Optional[str] = None) -> Tuple[Tensor, int]: r"""See torchaudio.load""" assert out is None @@ -96,7 +98,7 @@ def load( return out, sample_rate -def save(filepath, src, sample_rate, precision=16, channels_first=True): +def save(filepath: str, src: Tensor, sample_rate: int, precision: int = 16, channels_first: bool = True) -> None: r"""See torchaudio.save""" ch_idx, len_idx = (0, 1) if channels_first else (1, 0) @@ -129,7 +131,7 @@ def save(filepath, src, sample_rate, precision=16, channels_first=True): return soundfile.write(filepath, src, sample_rate, precision) -def info(filepath): +def info(filepath: str) -> Tuple[SignalInfo, EncodingInfo]: r"""See torchaudio.info""" import soundfile diff --git a/torchaudio/_sox_backend.py b/torchaudio/_sox_backend.py index 3aa66d94d5..802bd0fe17 100644 --- a/torchaudio/_sox_backend.py +++ b/torchaudio/_sox_backend.py @@ -1,21 +1,21 @@ import os.path +from typing import Optional, Tuple import torch - +from torch import Tensor import torchaudio - - -def load( - filepath, - out=None, - normalization=True, - channels_first=True, - num_frames=0, - offset=0, - signalinfo=None, - encodinginfo=None, - filetype=None, -): +from torchaudio._soundfile_backend import SignalInfo, EncodingInfo + + +def load(filepath: str, + out: Optional[Tensor] = None, + normalization: bool = True, + channels_first: bool = True, + num_frames: int = 0, + offset: int = 0, + signalinfo: SignalInfo = None, + encodinginfo: EncodingInfo = None, + filetype: Optional[str] = None) -> Tuple[Tensor, int]: r"""See torchaudio.load""" # stringify if `pathlib.Path` (noop if already `str`) @@ -53,7 +53,7 @@ def load( return out, sample_rate -def save(filepath, src, sample_rate, precision=16, channels_first=True): +def save(filepath: str, src: Tensor, sample_rate: int, precision: int = 16, channels_first: bool = True) -> None: r"""See torchaudio.save""" si = torchaudio.sox_signalinfo_t() @@ -65,7 +65,7 @@ def save(filepath, src, sample_rate, precision=16, channels_first=True): return torchaudio.save_encinfo(filepath, src, channels_first, si) -def info(filepath): +def info(filepath: str) -> Tuple[SignalInfo, EncodingInfo]: r"""See torchaudio.info""" import _torch_sox diff --git a/torchaudio/common_utils.py b/torchaudio/common_utils.py index 2321ad26a6..28b77200a6 100644 --- a/torchaudio/common_utils.py +++ b/torchaudio/common_utils.py @@ -4,7 +4,7 @@ PY34 = sys.version_info >= (3, 4) -def _check_module_exists(name): +def _check_module_exists(name: str) -> bool: r"""Returns if a top-level module with :attr:`name` exists *without** importing it. This is generally safer than try-catch block around a `import X`. It avoids third party libraries breaking assumptions of some of diff --git a/torchaudio/datasets/commonvoice.py b/torchaudio/datasets/commonvoice.py index 7d8e57c5fc..a11b9dc1c8 100644 --- a/torchaudio/datasets/commonvoice.py +++ b/torchaudio/datasets/commonvoice.py @@ -1,9 +1,10 @@ import os - -from torch.utils.data import Dataset +from typing import List, Dict, Tuple import torchaudio from torchaudio.datasets.utils import download_url, extract_archive, unicode_csv_reader +from torch import Tensor +from torch.utils.data import Dataset # Default TSV should be one of # dev.tsv @@ -17,9 +18,72 @@ URL = "english" VERSION = "cv-corpus-4-2019-12-10" TSV = "train.tsv" - - -def load_commonvoice_item(line, header, path, folder_audio): +_CHECKSUMS = { + "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/tt.tar.gz": + None, + "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/en.tar.gz": + None, + "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/de.tar.gz": + None, + "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/fr.tar.gz": + None, + "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/cy.tar.gz": + None, + "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/br.tar.gz": + None, + "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/cv.tar.gz": + None, + "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/tr.tar.gz": + None, + "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/ky.tar.gz": + None, + "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/ga-IE.tar.gz": + None, + "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/kab.tar.gz": + None, + "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/ca.tar.gz": + None, + "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/zh-TW.tar.gz": + None, + "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/sl.tar.gz": + None, + "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/it.tar.gz": + None, + "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/nl.tar.gz": + None, + "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/cnh.tar.gz": + None, + "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/eo.tar.gz": + None, + "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/et.tar.gz": + None, + "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/fa.tar.gz": + None, + "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/eu.tar.gz": + None, + "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/es.tar.gz": + None, + "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/zh-CN.tar.gz": + None, + "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/mn.tar.gz": + None, + "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/sah.tar.gz": + None, + "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/dv.tar.gz": + None, + "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/rw.tar.gz": + None, + "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/sv-SE.tar.gz": + None, + "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-3/ru.tar.gz": + None +} + + +def load_commonvoice_item(line: List[str], + header: List[str], + path: str, + folder_audio: str) -> Tuple[Tensor, int, Dict[str, str]]: # Each line as the following data: # client_id, path, sentence, up_votes, down_votes, age, gender, accent @@ -47,12 +111,13 @@ class COMMONVOICE(Dataset): _ext_audio = ".mp3" _folder_audio = "clips" - def __init__(self, root, - tsv=TSV, - url=URL, - folder_in_archive=FOLDER_IN_ARCHIVE, - version=VERSION, - download=False): + def __init__(self, + root: str, + tsv: str = TSV, + url: str = URL, + folder_in_archive: str = FOLDER_IN_ARCHIVE, + version: str = VERSION, + download: bool = False) -> None: languages = { "tatar": "tt", @@ -115,8 +180,9 @@ def __init__(self, root, if download: if not os.path.isdir(self._path): if not os.path.isfile(archive): - download_url(url, root) - extract_archive(archive, self._path) + checksum = _CHECKSUMS.get(url, None) + download_url(url, root, hash_value=checksum) + extract_archive(archive) self._tsv = os.path.join(root, folder_in_archive, tsv) @@ -125,9 +191,9 @@ def __init__(self, root, self._header = next(walker) self._walker = list(walker) - def __getitem__(self, n): + def __getitem__(self, n: int) -> Tuple[Tensor, int, Dict[str, str]]: line = self._walker[n] return load_commonvoice_item(line, self._header, self._path, self._folder_audio) - def __len__(self): + def __len__(self) -> int: return len(self._walker) diff --git a/torchaudio/datasets/librispeech.py b/torchaudio/datasets/librispeech.py index 8ca8c1af38..449ab744d3 100644 --- a/torchaudio/datasets/librispeech.py +++ b/torchaudio/datasets/librispeech.py @@ -1,20 +1,39 @@ import os +from typing import Tuple import torchaudio +from torch import Tensor from torch.utils.data import Dataset from torchaudio.datasets.utils import ( download_url, extract_archive, - unicode_csv_reader, walk_files, ) URL = "train-clean-100" FOLDER_IN_ARCHIVE = "LibriSpeech" - - -def load_librispeech_item(fileid, path, ext_audio, ext_txt): - +_CHECKSUMS = { + "http://www.openslr.org/resources/12/dev-clean.tar.gz": + "76f87d090650617fca0cac8f88b9416e0ebf80350acb97b343a85fa903728ab3", + "http://www.openslr.org/resources/12/dev-other.tar.gz": + "12661c48e8c3fe1de2c1caa4c3e135193bfb1811584f11f569dd12645aa84365", + "http://www.openslr.org/resources/12/test-clean.tar.gz": + "39fde525e59672dc6d1551919b1478f724438a95aa55f874b576be21967e6c23", + "http://www.openslr.org/resources/12/test-other.tar.gz": + "d09c181bba5cf717b3dee7d4d592af11a3ee3a09e08ae025c5506f6ebe961c29", + "http://www.openslr.org/resources/12/train-clean-100.tar.gz": + "d4ddd1d5a6ab303066f14971d768ee43278a5f2a0aa43dc716b0e64ecbbbf6e2", + "http://www.openslr.org/resources/12/train-clean-360.tar.gz": + "146a56496217e96c14334a160df97fffedd6e0a04e66b9c5af0d40be3c792ecf", + "http://www.openslr.org/resources/12/train-other-500.tar.gz": + "ddb22f27f96ec163645d53215559df6aa36515f26e01dd70798188350adcb6d2" +} + + +def load_librispeech_item(fileid: str, + path: str, + ext_audio: str, + ext_txt: str) -> Tuple[Tensor, int, str, int, int, int]: speaker_id, chapter_id, utterance_id = fileid.split("-") file_text = speaker_id + "-" + chapter_id + ext_txt @@ -56,9 +75,11 @@ class LIBRISPEECH(Dataset): _ext_txt = ".trans.txt" _ext_audio = ".flac" - def __init__( - self, root, url=URL, folder_in_archive=FOLDER_IN_ARCHIVE, download=False - ): + def __init__(self, + root: str, + url: str = URL, + folder_in_archive: str = FOLDER_IN_ARCHIVE, + download: bool = False) -> None: if url in [ "dev-clean", @@ -86,7 +107,8 @@ def __init__( if download: if not os.path.isdir(self._path): if not os.path.isfile(archive): - download_url(url, root) + checksum = _CHECKSUMS.get(url, None) + download_url(url, root, hash_value=checksum) extract_archive(archive) walker = walk_files( @@ -94,9 +116,9 @@ def __init__( ) self._walker = list(walker) - def __getitem__(self, n): + def __getitem__(self, n: int) -> Tuple[Tensor, int, str, int, int, int]: fileid = self._walker[n] return load_librispeech_item(fileid, self._path, self._ext_audio, self._ext_txt) - def __len__(self): + def __len__(self) -> int: return len(self._walker) diff --git a/torchaudio/datasets/ljspeech.py b/torchaudio/datasets/ljspeech.py index f08795277a..0b4ee86acc 100644 --- a/torchaudio/datasets/ljspeech.py +++ b/torchaudio/datasets/ljspeech.py @@ -1,15 +1,21 @@ import os import csv +from typing import List, Tuple import torchaudio from torchaudio.datasets.utils import download_url, extract_archive, unicode_csv_reader +from torch import Tensor from torch.utils.data import Dataset URL = "https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2" FOLDER_IN_ARCHIVE = "wavs" +_CHECKSUMS = { + "https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2": + "be1a30453f28eb8dd26af4101ae40cbf2c50413b1bb21936cbcdc6fae3de8aa5" +} -def load_ljspeech_item(line, path, ext_audio): +def load_ljspeech_item(line: List[str], path: str, ext_audio: str) -> Tuple[Tensor, int, str, str]: assert len(line) == 3 fileid, transcript, normalized_transcript = line fileid_audio = fileid + ext_audio @@ -35,9 +41,11 @@ class LJSPEECH(Dataset): _ext_audio = ".wav" _ext_archive = '.tar.bz2' - def __init__( - self, root, url=URL, folder_in_archive=FOLDER_IN_ARCHIVE, download=False - ): + def __init__(self, + root: str, + url: str = URL, + folder_in_archive: str = FOLDER_IN_ARCHIVE, + download: bool = False) -> None: basename = os.path.basename(url) archive = os.path.join(root, basename) @@ -51,16 +59,17 @@ def __init__( if download: if not os.path.isdir(self._path): if not os.path.isfile(archive): - download_url(url, root) + checksum = _CHECKSUMS.get(url, None) + download_url(url, root, hash_value=checksum) extract_archive(archive) with open(self._metadata_path, "r") as metadata: walker = unicode_csv_reader(metadata, delimiter="|", quoting=csv.QUOTE_NONE) self._walker = list(walker) - def __getitem__(self, n): + def __getitem__(self, n: int) -> Tuple[Tensor, int, str, str]: line = self._walker[n] return load_ljspeech_item(line, self._path, self._ext_audio) - def __len__(self): + def __len__(self) -> int: return len(self._walker) diff --git a/torchaudio/datasets/speechcommands.py b/torchaudio/datasets/speechcommands.py index 5c67eb060b..9943d5965b 100644 --- a/torchaudio/datasets/speechcommands.py +++ b/torchaudio/datasets/speechcommands.py @@ -1,7 +1,9 @@ import os +from typing import Tuple import torchaudio from torch.utils.data import Dataset +from torch import Tensor from torchaudio.datasets.utils import ( download_url, extract_archive, @@ -12,9 +14,15 @@ URL = "speech_commands_v0.02" HASH_DIVIDER = "_nohash_" EXCEPT_FOLDER = "_background_noise_" +_CHECKSUMS = { + "https://storage.googleapis.com/download.tensorflow.org/data/speech_commands_v0.01.tar.gz": + "3cd23799cb2bbdec517f1cc028f8d43c", + "https://storage.googleapis.com/download.tensorflow.org/data/speech_commands_v0.02.tar.gz": + "6b74f3901214cb2c2934e98196829835", +} -def load_speechcommands_item(filepath, path): +def load_speechcommands_item(filepath: str, path: str) -> Tuple[Tensor, int, str, str, int]: relpath = os.path.relpath(filepath, path) label, filename = os.path.split(relpath) speaker, _ = os.path.splitext(filename) @@ -33,13 +41,11 @@ class SPEECHCOMMANDS(Dataset): waveform, sample_rate, label, speaker_id, utterance_number """ - def __init__( - self, - root, - url=URL, - folder_in_archive=FOLDER_IN_ARCHIVE, - download=False - ): + def __init__(self, + root: str, + url: str = URL, + folder_in_archive: str = FOLDER_IN_ARCHIVE, + download: bool = False) -> None: if url in [ "speech_commands_v0.01", "speech_commands_v0.02", @@ -60,16 +66,17 @@ def __init__( if download: if not os.path.isdir(self._path): if not os.path.isfile(archive): - download_url(url, root) + checksum = _CHECKSUMS.get(url, None) + download_url(url, root, hash_value=checksum, hash_type="md5") extract_archive(archive, self._path) walker = walk_files(self._path, suffix=".wav", prefix=True) walker = filter(lambda w: HASH_DIVIDER in w and EXCEPT_FOLDER not in w, walker) self._walker = list(walker) - def __getitem__(self, n): + def __getitem__(self, n: int) -> Tuple[Tensor, int, str, str, int]: fileid = self._walker[n] return load_speechcommands_item(fileid, self._path) - def __len__(self): + def __len__(self) -> int: return len(self._walker) diff --git a/torchaudio/datasets/utils.py b/torchaudio/datasets/utils.py index f7d12baa2d..2840b43471 100644 --- a/torchaudio/datasets/utils.py +++ b/torchaudio/datasets/utils.py @@ -7,20 +7,24 @@ import tarfile import threading import zipfile +from _io import TextIOWrapper from queue import Queue +from typing import Any, Iterable, List, Optional, Tuple, Union import torch import urllib +import urllib.request from torch.utils.data import Dataset from torch.utils.model_zoo import tqdm -def unicode_csv_reader(unicode_csv_data, **kwargs): +def unicode_csv_reader(unicode_csv_data: TextIOWrapper, **kwargs: Any) -> Any: r"""Since the standard csv library does not handle unicode in Python 2, we need a wrapper. Borrowed and slightly modified from the Python docs: https://docs.python.org/2/library/csv.html#csv-examples - Arguments: - unicode_csv_data: unicode csv data (see example below) + Args: + unicode_csv_data (TextIOWrapper): unicode csv data (see example below) + Examples: >>> from torchaudio.datasets.utils import unicode_csv_reader >>> import io @@ -44,7 +48,7 @@ def unicode_csv_reader(unicode_csv_data, **kwargs): yield line -def makedir_exist_ok(dirpath): +def makedir_exist_ok(dirpath: str) -> None: """ Python2 support for os.makedirs(.., exist_ok=True) """ @@ -57,14 +61,17 @@ def makedir_exist_ok(dirpath): raise -def stream_url(url, start_byte=None, block_size=32 * 1024, progress_bar=True): +def stream_url(url: str, + start_byte: Optional[int] = None, + block_size: int = 32 * 1024, + progress_bar: bool = True) -> Iterable: """Stream url by chunk Args: url (str): Url. - start_byte (Optional[int]): Start streaming at that point. - block_size (int): Size of chunks to stream. - progress_bar (bool): Display a progress bar. + start_byte (int, optional): Start streaming at that point (Default: ``None``). + block_size (int, optional): Size of chunks to stream (Default: ``32 * 1024``). + progress_bar (bool, optional): Display a progress bar (Default: ``True``). """ # If we already have the whole file, there is no need to download it again @@ -95,25 +102,23 @@ def stream_url(url, start_byte=None, block_size=32 * 1024, progress_bar=True): pbar.update(len(chunk)) -def download_url( - url, - download_folder, - filename=None, - hash_value=None, - hash_type="sha256", - progress_bar=True, - resume=False, -): +def download_url(url: str, + download_folder: str, + filename: Optional[str] = None, + hash_value: Optional[str] = None, + hash_type: str = "sha256", + progress_bar: bool = True, + resume: bool = False) -> None: """Download file to disk. Args: url (str): Url. download_folder (str): Folder to download file. - filename (str): Name of downloaded file. If None, it is inferred from the url. - hash_value (str): Hash for url. - hash_type (str): Hash type, among "sha256" and "md5". - progress_bar (bool): Display a progress bar. - resume (bool): Enable resuming download. + filename (str, optional): Name of downloaded file. If None, it is inferred from the url (Default: ``None``). + hash_value (str, optional): Hash for url (Default: ``None``). + hash_type (str, optional): Hash type, among "sha256" and "md5" (Default: ``"sha256"``). + progress_bar (bool, optional): Display a progress bar (Default: ``True``). + resume (bool, optional): Enable resuming download (Default: ``False``). """ req = urllib.request.Request(url, method="HEAD") @@ -122,10 +127,10 @@ def download_url( # Detect filename filename = filename or req_info.get_filename() or os.path.basename(url) filepath = os.path.join(download_folder, filename) - if resume and os.path.exists(filepath): mode = "ab" - local_size = os.path.getsize(filepath) + local_size: Optional[int] = os.path.getsize(filepath) + elif not resume and os.path.exists(filepath): raise RuntimeError( "{} already exists. Delete the file manually and retry.".format(filepath) @@ -157,13 +162,16 @@ def download_url( ) -def validate_file(file_obj, hash_value, hash_type="sha256"): +def validate_file(file_obj: Any, hash_value: str, hash_type: str = "sha256") -> bool: """Validate a given file object with its hash. Args: file_obj: File object to read from. hash_value (str): Hash for url. - hash_type (str): Hash type, among "sha256" and "md5". + hash_type (str, optional): Hash type, among "sha256" and "md5" (Default: ``"sha256"``). + + Returns: + bool: return True if its a valid file, else False. """ if hash_type == "sha256": @@ -183,14 +191,16 @@ def validate_file(file_obj, hash_value, hash_type="sha256"): return hash_func.hexdigest() == hash_value -def extract_archive(from_path, to_path=None, overwrite=False): +def extract_archive(from_path: str, to_path: Optional[str] = None, overwrite: bool = False) -> List[str]: """Extract archive. - Arguments: - from_path: the path of the archive. - to_path: the root path of the extraced files (directory of from_path) - overwrite: overwrite existing files (False) + Args: + from_path (str): the path of the archive. + to_path (str, optional): the root path of the extraced files (directory of from_path) (Default: ``None``) + overwrite (bool, optional): overwrite existing files (Default: ``False``) + Returns: - List of paths to extracted files even if not overwritten. + list: List of paths to extracted files even if not overwritten. + Examples: >>> url = 'http://www.quest.dcs.shef.ac.uk/wmt16_files_mmt/validation.tar.gz' >>> from_path = './validation.tar.gz' @@ -206,7 +216,7 @@ def extract_archive(from_path, to_path=None, overwrite=False): with tarfile.open(from_path, "r") as tar: logging.info("Opened tar file {}.".format(from_path)) files = [] - for file_ in tar: + for file_ in tar: # type: Any file_path = os.path.join(to_path, file_.name) if file_.isfile(): files.append(file_path) @@ -237,7 +247,10 @@ def extract_archive(from_path, to_path=None, overwrite=False): raise NotImplementedError("We currently only support tar.gz, tgz, and zip achives.") -def walk_files(root, suffix, prefix=False, remove_suffix=False): +def walk_files(root: str, + suffix: Union[str, Tuple[str]], + prefix: bool = False, + remove_suffix: bool = False) -> Iterable[str]: """List recursively all files ending with a suffix at a given root Args: root (str): Path to directory whose folders need to be listed @@ -269,14 +282,14 @@ class _DiskCache(Dataset): Wrap a dataset so that, whenever a new item is returned, it is saved to disk. """ - def __init__(self, dataset, location=".cached"): + def __init__(self, dataset: Dataset, location: str = ".cached") -> None: self.dataset = dataset self.location = location self._id = id(self) - self._cache = [None] * len(dataset) + self._cache: List = [None] * len(dataset) - def __getitem__(self, n): + def __getitem__(self, n: int) -> Any: if self._cache[n]: f = self._cache[n] return torch.load(f) @@ -291,11 +304,11 @@ def __getitem__(self, n): return item - def __len__(self): + def __len__(self) -> int: return len(self.dataset) -def diskcache_iterator(dataset, location=".cached"): +def diskcache_iterator(dataset: Dataset, location: str = ".cached") -> Dataset: return _DiskCache(dataset, location) @@ -311,31 +324,31 @@ class _ThreadedIterator(threading.Thread): class _End: pass - def __init__(self, generator, maxsize): + def __init__(self, generator: Iterable, maxsize: int) -> None: threading.Thread.__init__(self) - self.queue = Queue(maxsize) + self.queue: Queue = Queue(maxsize) self.generator = generator self.daemon = True self.start() - def run(self): + def run(self) -> None: for item in self.generator: self.queue.put(item) self.queue.put(self._End) - def __iter__(self): + def __iter__(self) -> Any: return self - def __next__(self): + def __next__(self) -> Any: next_item = self.queue.get() if next_item == self._End: raise StopIteration return next_item # Required for Python 2.7 compatibility - def next(self): + def next(self) -> Any: return self.__next__() -def bg_iterator(iterable, maxsize): +def bg_iterator(iterable: Iterable, maxsize: int) -> Any: return _ThreadedIterator(iterable, maxsize=maxsize) diff --git a/torchaudio/datasets/vctk.py b/torchaudio/datasets/vctk.py index 87f51d68a0..c84373713d 100644 --- a/torchaudio/datasets/vctk.py +++ b/torchaudio/datasets/vctk.py @@ -1,17 +1,31 @@ import os import warnings +from typing import Any, Tuple import torchaudio +from torch import Tensor from torch.utils.data import Dataset -from torchaudio.datasets.utils import download_url, extract_archive, walk_files +from torchaudio.datasets.utils import ( + download_url, + extract_archive, + walk_files +) URL = "http://homepages.inf.ed.ac.uk/jyamagis/release/VCTK-Corpus.tar.gz" FOLDER_IN_ARCHIVE = "VCTK-Corpus" - - -def load_vctk_item( - fileid, path, ext_audio, ext_txt, folder_audio, folder_txt, downsample=False -): +_CHECKSUMS = { + "http://homepages.inf.ed.ac.uk/jyamagis/release/VCTK-Corpus.tar.gz": + "45e8dede780278ef5541fde0b82ac292" +} + + +def load_vctk_item(fileid: str, + path: str, + ext_audio: str, + ext_txt: str, + folder_audio: str, + folder_txt: str, + downsample: bool = False) -> Tuple[Tensor, int, str, str, str]: speaker_id, utterance_id = fileid.split("_") # Read text @@ -50,21 +64,19 @@ class VCTK(Dataset): _ext_audio = ".wav" _except_folder = "p315" - def __init__( - self, - root, - url=URL, - folder_in_archive=FOLDER_IN_ARCHIVE, - download=False, - downsample=False, - transform=None, - target_transform=None, - ): + def __init__(self, + root: str, + url: str = URL, + folder_in_archive: str = FOLDER_IN_ARCHIVE, + download: bool = False, + downsample: bool = False, + transform: Any = None, + target_transform: Any = None) -> None: if downsample: warnings.warn( "In the next version, transforms will not be part of the dataset. " - "Please use `downsample=False` to enable this behavior now, ", + "Please use `downsample=False` to enable this behavior now, " "and suppress this warning." ) @@ -86,7 +98,8 @@ def __init__( if download: if not os.path.isdir(self._path): if not os.path.isfile(archive): - download_url(url, root) + checksum = _CHECKSUMS.get(url, None) + download_url(url, root, hash_value=checksum, hash_type="md5") extract_archive(archive) if not os.path.isdir(self._path): @@ -100,7 +113,7 @@ def __init__( walker = filter(lambda w: self._except_folder not in w, walker) self._walker = list(walker) - def __getitem__(self, n): + def __getitem__(self, n: int) -> Tuple[Tensor, int, str, str, str]: fileid = self._walker[n] item = load_vctk_item( fileid, @@ -121,5 +134,5 @@ def __getitem__(self, n): utterance = self.target_transform(utterance) return waveform, sample_rate, utterance, speaker_id, utterance_id - def __len__(self): + def __len__(self) -> int: return len(self._walker) diff --git a/torchaudio/datasets/yesno.py b/torchaudio/datasets/yesno.py index 7d0fe58f4c..e45b157c35 100644 --- a/torchaudio/datasets/yesno.py +++ b/torchaudio/datasets/yesno.py @@ -1,15 +1,25 @@ import os import warnings +from typing import Any, List, Tuple import torchaudio +from torch import Tensor from torch.utils.data import Dataset -from torchaudio.datasets.utils import download_url, extract_archive, walk_files +from torchaudio.datasets.utils import ( + download_url, + extract_archive, + walk_files +) URL = "http://www.openslr.org/resources/1/waves_yesno.tar.gz" FOLDER_IN_ARCHIVE = "waves_yesno" +_CHECKSUMS = { + "http://www.openslr.org/resources/1/waves_yesno.tar.gz": + "962ff6e904d2df1126132ecec6978786" +} -def load_yesno_item(fileid, path, ext_audio): +def load_yesno_item(fileid: str, path: str, ext_audio: str) -> Tuple[Tensor, int, List[int]]: # Read label labels = [int(c) for c in fileid.split("_")] @@ -28,15 +38,13 @@ class YESNO(Dataset): _ext_audio = ".wav" - def __init__( - self, - root, - url=URL, - folder_in_archive=FOLDER_IN_ARCHIVE, - download=False, - transform=None, - target_transform=None, - ): + def __init__(self, + root: str, + url: str = URL, + folder_in_archive: str = FOLDER_IN_ARCHIVE, + download: bool = False, + transform: Any = None, + target_transform: Any = None) -> None: if transform is not None or target_transform is not None: warnings.warn( @@ -55,7 +63,8 @@ def __init__( if download: if not os.path.isdir(self._path): if not os.path.isfile(archive): - download_url(url, root) + checksum = _CHECKSUMS.get(url, None) + download_url(url, root, hash_value=checksum, hash_type="md5") extract_archive(archive) if not os.path.isdir(self._path): @@ -68,7 +77,7 @@ def __init__( ) self._walker = list(walker) - def __getitem__(self, n): + def __getitem__(self, n: int) -> Tuple[Tensor, int, List[int]]: fileid = self._walker[n] item = load_yesno_item(fileid, self._path, self._ext_audio) @@ -82,5 +91,5 @@ def __getitem__(self, n): labels = self.target_transform(labels) return waveform, sample_rate, labels - def __len__(self): + def __len__(self) -> int: return len(self._walker) diff --git a/torchaudio/functional.py b/torchaudio/functional.py index 282470fb4c..cb5156596d 100644 --- a/torchaudio/functional.py +++ b/torchaudio/functional.py @@ -31,37 +31,16 @@ "deemph_biquad", "riaa_biquad", "biquad", + "contrast", + "dcshift", + "overdrive", 'mask_along_axis', - 'mask_along_axis_iid' + 'mask_along_axis_iid', + 'sliding_window_cmn', + 'vad', ] -# TODO: remove this once https://github.com/pytorch/pytorch/issues/21478 gets solved -@torch.jit.ignore -def _stft( - waveform: Tensor, - n_fft: int, - hop_length: Optional[int], - win_length: Optional[int], - window: Optional[Tensor], - center: bool, - pad_mode: str, - normalized: bool, - onesided: bool -) -> Tensor: - return torch.stft( - waveform, - n_fft, - hop_length, - win_length, - window, - center, - pad_mode, - normalized, - onesided, - ) - - def istft( stft_matrix: Tensor, n_fft: int, @@ -265,7 +244,7 @@ def spectrogram( waveform = waveform.view(-1, shape[-1]) # default values are consistent with librosa.core.spectrum._spectrogram - spec_f = _stft( + spec_f = torch.stft( waveform, n_fft, hop_length, win_length, window, True, "reflect", False, True ) @@ -365,8 +344,8 @@ def griffinlim( length=length).float() # Rebuild the spectrogram - rebuilt = _stft(inverse, n_fft, hop_length, win_length, window, - True, 'reflect', False, True) + rebuilt = torch.stft(inverse, n_fft, hop_length, win_length, window, + True, 'reflect', False, True) # Update our phase estimates angles = rebuilt - tprev.mul_(momentum / (1 + momentum)) @@ -743,11 +722,11 @@ def lfilter( # (n_order, ) matmul (n_channel, n_order, n_sample) -> (n_channel, n_sample) input_signal_windows = torch.matmul(b_coeffs_flipped, torch.take(padded_waveform, window_idxs)) + input_signal_windows.div_(a_coeffs[0]) + a_coeffs_flipped.div_(a_coeffs[0]) for i_sample, o0 in enumerate(input_signal_windows.t()): windowed_output_signal = padded_output_waveform[:, i_sample:(i_sample + n_order)] - o0.sub_(torch.mv(windowed_output_signal, a_coeffs_flipped)) - o0.div_(a_coeffs[0]) - + o0.addmv_(windowed_output_signal, a_coeffs_flipped, alpha=-1) padded_output_waveform[:, i_sample + n_order - 1] = o0 output = torch.clamp(padded_output_waveform[:, (n_order - 1):], min=-1., max=1.) @@ -1186,6 +1165,137 @@ def riaa_biquad( return biquad(waveform, b0, b1, b2, a0, a1, a2) +def contrast( + waveform: Tensor, + enhancement_amount: float = 75. +) -> Tensor: + r"""Apply contrast effect. Similar to SoX implementation. + Comparable with compression, this effect modifies an audio signal to make it sound louder + + Args: + waveform (Tensor): audio waveform of dimension of `(..., time)` + enhancement_amount (float): controls the amount of the enhancement + Allowed range of values for enhancement_amount : 0-100 + Note that enhancement_amount = 0 still gives a significant contrast enhancement + + Returns: + Tensor: Waveform of dimension of `(..., time)` + + References: + http://sox.sourceforge.net/sox.html + """ + + if not 0 <= enhancement_amount <= 100: + raise ValueError("Allowed range of values for enhancement_amount : 0-100") + + contrast = enhancement_amount / 750. + + temp1 = waveform * (math.pi / 2) + temp2 = contrast * torch.sin(temp1 * 4) + output_waveform = torch.sin(temp1 + temp2) + + return output_waveform + + +def dcshift( + waveform: Tensor, + shift: float, + limiter_gain: Optional[float] = None +) -> Tensor: + r"""Apply a DC shift to the audio. Similar to SoX implementation. + This can be useful to remove a DC offset + (caused perhaps by a hardware problem in the recording chain) from the audio + + Args: + waveform (Tensor): audio waveform of dimension of `(..., time)` + shift (float): indicates the amount to shift the audio + Allowed range of values for shift : -2.0 to +2.0 + limiter_gain (float): It is used only on peaks to prevent clipping + It should have a value much less than 1 (e.g. 0.05 or 0.02) + + Returns: + Tensor: Waveform of dimension of `(..., time)` + + References: + http://sox.sourceforge.net/sox.html + """ + output_waveform = waveform + limiter_threshold = 0. + + if limiter_gain is not None: + limiter_threshold = 1.0 - (abs(shift) - limiter_gain) + + if limiter_gain is not None and shift > 0: + mask = waveform > limiter_threshold + temp = (waveform[mask] - limiter_threshold) * limiter_gain / (1 - limiter_threshold) + output_waveform[mask] = (temp + limiter_threshold + shift).clamp(max=limiter_threshold) + output_waveform[~mask] = (waveform[~mask] + shift).clamp(min=-1, max=1) + elif limiter_gain is not None and shift < 0: + mask = waveform < -limiter_threshold + temp = (waveform[mask] + limiter_threshold) * limiter_gain / (1 - limiter_threshold) + output_waveform[mask] = (temp - limiter_threshold + shift).clamp(min=-limiter_threshold) + output_waveform[~mask] = (waveform[~mask] + shift).clamp(min=-1, max=1) + else: + output_waveform = (waveform + shift).clamp(min=-1, max=1) + + return output_waveform + + +def overdrive( + waveform: Tensor, + gain: float = 20, + colour: float = 20 +) -> Tensor: + r"""Apply a overdrive effect to the audio. Similar to SoX implementation. + This effect applies a non linear distortion to the audio signal. + + Args: + waveform (Tensor): audio waveform of dimension of `(..., time)` + gain (float): desired gain at the boost (or attenuation) in dB + Allowed range of values are 0 to 100 + colour (float): controls the amount of even harmonic content in the over-driven output + Allowed range of values are 0 to 100 + + Returns: + Tensor: Waveform of dimension of `(..., time)` + + References: + http://sox.sourceforge.net/sox.html + """ + actual_shape = waveform.shape + device, dtype = waveform.device, waveform.dtype + + # convert to 2D (..,time) + waveform = waveform.view(-1, actual_shape[-1]) + + gain = _dB2Linear(gain) + colour = colour / 200 + last_in = torch.zeros(waveform.shape[:-1], dtype=dtype, device=device) + last_out = torch.zeros(waveform.shape[:-1], dtype=dtype, device=device) + + temp = waveform * gain + colour + + mask1 = temp < -1 + temp[mask1] = torch.tensor(-2.0 / 3.0, dtype=dtype, device=device) + # Wrapping the constant with Tensor is required for Torchscript + + mask2 = temp > 1 + temp[mask2] = torch.tensor(2.0 / 3.0, dtype=dtype, device=device) + + mask3 = (~mask1 & ~mask2) + temp[mask3] = temp[mask3] - (temp[mask3]**3) * (1. / 3) + + output_waveform = torch.zeros_like(waveform, dtype=dtype, device=device) + + # TODO: Implement a torch CPP extension + for i in range(waveform.shape[-1]): + last_out = temp[:, i] - last_in + 0.995 * last_out + last_in = temp[:, i] + output_waveform[:, i] = waveform[:, i] * 0.5 + last_out * 0.75 + + return output_waveform.clamp(min=-1, max=1).view(actual_shape) + + def mask_along_axis_iid( specgrams: Tensor, mask_param: int, @@ -1210,13 +1320,16 @@ def mask_along_axis_iid( if axis != 2 and axis != 3: raise ValueError('Only Frequency and Time masking are supported') - value = torch.rand(specgrams.shape[:2]) * mask_param - min_value = torch.rand(specgrams.shape[:2]) * (specgrams.size(axis) - value) + device = specgrams.device + dtype = specgrams.dtype + + value = torch.rand(specgrams.shape[:2], device=device, dtype=dtype) * mask_param + min_value = torch.rand(specgrams.shape[:2], device=device, dtype=dtype) * (specgrams.size(axis) - value) # Create broadcastable mask - mask_start = (min_value.long())[..., None, None].float() - mask_end = (min_value.long() + value.long())[..., None, None].float() - mask = torch.arange(0, specgrams.size(axis)).float() + mask_start = min_value[..., None, None] + mask_end = (min_value + value)[..., None, None] + mask = torch.arange(0, specgrams.size(axis), device=device, dtype=dtype) # Per batch example masking specgrams = specgrams.transpose(axis, -1) @@ -1298,6 +1411,8 @@ def compute_deltas( >>> delta = compute_deltas(specgram) >>> delta2 = compute_deltas(delta) """ + device = specgram.device + dtype = specgram.dtype # pack batch shape = specgram.size() @@ -1312,7 +1427,7 @@ def compute_deltas( specgram = torch.nn.functional.pad(specgram, (n, n), mode=mode) - kernel = (torch.arange(-n, n + 1, 1, device=specgram.device, dtype=specgram.dtype).repeat(specgram.shape[1], 1, 1)) + kernel = torch.arange(-n, n + 1, 1, device=device, dtype=dtype).repeat(specgram.shape[1], 1, 1) output = torch.nn.functional.conv1d(specgram, kernel, groups=specgram.shape[1]) / denom @@ -1431,7 +1546,7 @@ def _apply_probability_distribution( signal_scaled_dis = signal_scaled + gaussian else: # dtype needed for https://github.com/pytorch/pytorch/issues/32358 - TPDF = torch.bartlett_window(time_size + 1, dtype=torch.float) + TPDF = torch.bartlett_window(time_size + 1, dtype=signal_scaled.dtype, device=signal_scaled.device) TPDF = TPDF.repeat((channel_size + 1), 1) signal_scaled_dis = signal_scaled + TPDF @@ -1631,3 +1746,390 @@ def detect_pitch_frequency( freq = freq.view(shape[:-1] + list(freq.shape[-1:])) return freq + + +def sliding_window_cmn( + waveform: Tensor, + cmn_window: int = 600, + min_cmn_window: int = 100, + center: bool = False, + norm_vars: bool = False, +) -> Tensor: + r""" + Apply sliding-window cepstral mean (and optionally variance) normalization per utterance. + + Args: + waveform (Tensor): Tensor of audio of dimension (..., freq, time) + cmn_window (int, optional): Window in frames for running average CMN computation (int, default = 600) + min_cmn_window (int, optional): Minimum CMN window used at start of decoding (adds latency only at start). + Only applicable if center == false, ignored if center==true (int, default = 100) + center (bool, optional): If true, use a window centered on the current frame + (to the extent possible, modulo end effects). If false, window is to the left. (bool, default = false) + norm_vars (bool, optional): If true, normalize variance to one. (bool, default = false) + + Returns: + Tensor: Tensor of freq of dimension (..., frame) + """ + input_shape = waveform.shape + num_frames, num_feats = input_shape[-2:] + waveform = waveform.view(-1, num_frames, num_feats) + num_channels = waveform.shape[0] + + dtype = waveform.dtype + device = waveform.device + last_window_start = last_window_end = -1 + cur_sum = torch.zeros(num_channels, num_feats, dtype=dtype, device=device) + cur_sumsq = torch.zeros(num_channels, num_feats, dtype=dtype, device=device) + cmn_waveform = torch.zeros( + num_channels, num_frames, num_feats, dtype=dtype, device=device) + for t in range(num_frames): + window_start = 0 + window_end = 0 + if center: + window_start = t - cmn_window // 2 + window_end = window_start + cmn_window + else: + window_start = t - cmn_window + window_end = t + 1 + if window_start < 0: + window_end -= window_start + window_start = 0 + if not center: + if window_end > t: + window_end = max(t + 1, min_cmn_window) + if window_end > num_frames: + window_start -= (window_end - num_frames) + window_end = num_frames + if window_start < 0: + window_start = 0 + if last_window_start == -1: + input_part = waveform[:, window_start: window_end - window_start, :] + cur_sum += torch.sum(input_part, 1) + if norm_vars: + cur_sumsq += torch.cumsum(input_part ** 2, 1)[:, -1, :] + else: + if window_start > last_window_start: + frame_to_remove = waveform[:, last_window_start, :] + cur_sum -= frame_to_remove + if norm_vars: + cur_sumsq -= (frame_to_remove ** 2) + if window_end > last_window_end: + frame_to_add = waveform[:, last_window_end, :] + cur_sum += frame_to_add + if norm_vars: + cur_sumsq += (frame_to_add ** 2) + window_frames = window_end - window_start + last_window_start = window_start + last_window_end = window_end + cmn_waveform[:, t, :] = waveform[:, t, :] - cur_sum / window_frames + if norm_vars: + if window_frames == 1: + cmn_waveform[:, t, :] = torch.zeros( + num_channels, num_feats, dtype=dtype, device=device) + else: + variance = cur_sumsq + variance = variance / window_frames + variance -= ((cur_sum ** 2) / (window_frames ** 2)) + variance = torch.pow(variance, -0.5) + cmn_waveform[:, t, :] *= variance + + cmn_waveform = cmn_waveform.view(input_shape[:-2] + (num_frames, num_feats)) + if len(input_shape) == 2: + cmn_waveform = cmn_waveform.squeeze(0) + return cmn_waveform + + +def _measure( + measure_len_ws: int, + samples: Tensor, + spectrum: Tensor, + noise_spectrum: Tensor, + spectrum_window: Tensor, + spectrum_start: int, + spectrum_end: int, + cepstrum_window: Tensor, + cepstrum_start: int, + cepstrum_end: int, + noise_reduction_amount: float, + measure_smooth_time_mult: float, + noise_up_time_mult: float, + noise_down_time_mult: float, + index_ns: int, + boot_count: int +) -> float: + + assert spectrum.size()[-1] == noise_spectrum.size()[-1] + + samplesLen_ns = samples.size()[-1] + dft_len_ws = spectrum.size()[-1] + + dftBuf = torch.zeros(dft_len_ws) + + _index_ns = torch.tensor([index_ns] + [ + (index_ns + i) % samplesLen_ns + for i in range(1, measure_len_ws) + ]) + dftBuf[:measure_len_ws] = \ + samples[_index_ns] * spectrum_window[:measure_len_ws] + + # memset(c->dftBuf + i, 0, (p->dft_len_ws - i) * sizeof(*c->dftBuf)); + dftBuf[measure_len_ws:dft_len_ws].zero_() + + # lsx_safe_rdft((int)p->dft_len_ws, 1, c->dftBuf); + _dftBuf = torch.rfft(dftBuf, 1) + + # memset(c->dftBuf, 0, p->spectrum_start * sizeof(*c->dftBuf)); + _dftBuf[:spectrum_start].zero_() + + mult: float = boot_count / (1. + boot_count) \ + if boot_count >= 0 \ + else measure_smooth_time_mult + + _d = complex_norm(_dftBuf[spectrum_start:spectrum_end]) + spectrum[spectrum_start:spectrum_end].mul_(mult).add_(_d * (1 - mult)) + _d = spectrum[spectrum_start:spectrum_end] ** 2 + + _zeros = torch.zeros(spectrum_end - spectrum_start) + _mult = _zeros \ + if boot_count >= 0 \ + else torch.where( + _d > noise_spectrum[spectrum_start:spectrum_end], + torch.tensor(noise_up_time_mult), # if + torch.tensor(noise_down_time_mult) # else + ) + + noise_spectrum[spectrum_start:spectrum_end].mul_(_mult).add_(_d * (1 - _mult)) + _d = torch.sqrt( + torch.max( + _zeros, + _d - noise_reduction_amount * noise_spectrum[spectrum_start:spectrum_end])) + + _cepstrum_Buf: Tensor = torch.zeros(dft_len_ws >> 1) + _cepstrum_Buf[spectrum_start:spectrum_end] = _d * cepstrum_window + _cepstrum_Buf[spectrum_end:dft_len_ws >> 1].zero_() + + # lsx_safe_rdft((int)p->dft_len_ws >> 1, 1, c->dftBuf); + _cepstrum_Buf = torch.rfft(_cepstrum_Buf, 1) + + result: float = float(torch.sum( + complex_norm( + _cepstrum_Buf[cepstrum_start:cepstrum_end], + power=2.0))) + result = \ + math.log(result / (cepstrum_end - cepstrum_start)) \ + if result > 0 \ + else -math.inf + return max(0, 21 + result) + + +def vad( + waveform: Tensor, + sample_rate: int, + trigger_level: float = 7.0, + trigger_time: float = 0.25, + search_time: float = 1.0, + allowed_gap: float = 0.25, + pre_trigger_time: float = 0.0, + # Fine-tuning parameters + boot_time: float = .35, + noise_up_time: float = .1, + noise_down_time: float = .01, + noise_reduction_amount: float = 1.35, + measure_freq: float = 20.0, + measure_duration: Optional[float] = None, + measure_smooth_time: float = .4, + hp_filter_freq: float = 50., + lp_filter_freq: float = 6000., + hp_lifter_freq: float = 150., + lp_lifter_freq: float = 2000., +) -> Tensor: + r"""Voice Activity Detector. Similar to SoX implementation. + Attempts to trim silence and quiet background sounds from the ends of recordings of speech. + The algorithm currently uses a simple cepstral power measurement to detect voice, + so may be fooled by other things, especially music. + + The effect can trim only from the front of the audio, + so in order to trim from the back, the reverse effect must also be used. + + Args: + waveform (Tensor): Tensor of audio of dimension `(..., time)` + sample_rate (int): Sample rate of audio signal. + trigger_level (float, optional): The measurement level used to trigger activity detection. + This may need to be cahnged depending on the noise level, signal level, + and other characteristics of the input audio. (Default: 7.0) + trigger_time (float, optional): The time constant (in seconds) + used to help ignore short bursts of sound. (Default: 0.25) + search_time (float, optional): The amount of audio (in seconds) + to search for quieter/shorter bursts of audio to include prior + to the detected trigger point. (Default: 1.0) + allowed_gap (float, optional): The allowed gap (in seconds) between + quiteter/shorter bursts of audio to include prior + to the detected trigger point. (Default: 0.25) + pre_trigger_time (float, optional): The amount of audio (in seconds) to preserve + before the trigger point and any found quieter/shorter bursts. (Default: 0.0) + boot_time (float, optional) The algorithm (internally) uses adaptive noise + estimation/reduction in order to detect the start of the wanted audio. + This option sets the time for the initial noise estimate. (Default: 0.35) + noise_up_time (float, optional) Time constant used by the adaptive noise estimator + for when the noise level is increasing. (Default: 0.1) + noise_down_time (float, optional) Time constant used by the adaptive noise estimator + for when the noise level is decreasing. (Default: 0.01) + noise_reduction_amount (float, optional) Amount of noise reduction to use in + the detection algorithm (e.g. 0, 0.5, ...). (Default: 1.35) + measure_freq (float, optional) Frequency of the algorithmโ€™s + processing/measurements. (Default: 20.0) + measure_duration: (float, optional) Measurement duration. + (Default: Twice the measurement period; i.e. with overlap.) + measure_smooth_time (float, optional) Time constant used to smooth + spectral measurements. (Default: 0.4) + hp_filter_freq (float, optional) "Brick-wall" frequency of high-pass filter applied + at the input to the detector algorithm. (Default: 50.0) + lp_filter_freq (float, optional) "Brick-wall" frequency of low-pass filter applied + at the input to the detector algorithm. (Default: 6000.0) + hp_lifter_freq (float, optional) "Brick-wall" frequency of high-pass lifter used + in the detector algorithm. (Default: 150.0) + lp_lifter_freq (float, optional) "Brick-wall" frequency of low-pass lifter used + in the detector algorithm. (Default: 2000.0) + + Returns: + Tensor: Tensor of audio of dimension (..., time). + + References: + http://sox.sourceforge.net/sox.html + """ + + measure_duration: float = 2.0 / measure_freq \ + if measure_duration is None \ + else measure_duration + + measure_len_ws = int(sample_rate * measure_duration + .5) + measure_len_ns = measure_len_ws + # for (dft_len_ws = 16; dft_len_ws < measure_len_ws; dft_len_ws <<= 1); + dft_len_ws = 16 + while (dft_len_ws < measure_len_ws): + dft_len_ws *= 2 + + measure_period_ns = int(sample_rate / measure_freq + .5) + measures_len = math.ceil(search_time * measure_freq) + search_pre_trigger_len_ns = measures_len * measure_period_ns + gap_len = int(allowed_gap * measure_freq + .5) + + fixed_pre_trigger_len_ns = int(pre_trigger_time * sample_rate + .5) + samplesLen_ns = fixed_pre_trigger_len_ns + search_pre_trigger_len_ns + measure_len_ns + + spectrum_window = torch.zeros(measure_len_ws) + for i in range(measure_len_ws): + # sox.h:741 define SOX_SAMPLE_MIN (sox_sample_t)SOX_INT_MIN(32) + spectrum_window[i] = 2. / math.sqrt(float(measure_len_ws)) + # lsx_apply_hann(spectrum_window, (int)measure_len_ws); + spectrum_window *= torch.hann_window(measure_len_ws, dtype=torch.float) + + spectrum_start: int = int(hp_filter_freq / sample_rate * dft_len_ws + .5) + spectrum_start: int = max(spectrum_start, 1) + spectrum_end: int = int(lp_filter_freq / sample_rate * dft_len_ws + .5) + spectrum_end: int = min(spectrum_end, dft_len_ws // 2) + + cepstrum_window = torch.zeros(spectrum_end - spectrum_start) + for i in range(spectrum_end - spectrum_start): + cepstrum_window[i] = 2. / math.sqrt(float(spectrum_end) - spectrum_start) + # lsx_apply_hann(cepstrum_window,(int)(spectrum_end - spectrum_start)); + cepstrum_window *= torch.hann_window(spectrum_end - spectrum_start, dtype=torch.float) + + cepstrum_start = math.ceil(sample_rate * .5 / lp_lifter_freq) + cepstrum_end = math.floor(sample_rate * .5 / hp_lifter_freq) + cepstrum_end = min(cepstrum_end, dft_len_ws // 4) + + assert cepstrum_end > cepstrum_start + + noise_up_time_mult = math.exp(-1. / (noise_up_time * measure_freq)) + noise_down_time_mult = math.exp(-1. / (noise_down_time * measure_freq)) + measure_smooth_time_mult = math.exp(-1. / (measure_smooth_time * measure_freq)) + trigger_meas_time_mult = math.exp(-1. / (trigger_time * measure_freq)) + + boot_count_max = int(boot_time * measure_freq - .5) + measure_timer_ns = measure_len_ns + boot_count = measures_index = flushedLen_ns = samplesIndex_ns = 0 + + # pack batch + shape = waveform.size() + waveform = waveform.view(-1, shape[-1]) + + n_channels, ilen = waveform.size() + + mean_meas = torch.zeros(n_channels) + samples = torch.zeros(n_channels, samplesLen_ns) + spectrum = torch.zeros(n_channels, dft_len_ws) + noise_spectrum = torch.zeros(n_channels, dft_len_ws) + measures = torch.zeros(n_channels, measures_len) + + has_triggered: bool = False + num_measures_to_flush: int = 0 + pos: int = 0 + + while (pos < ilen and not has_triggered): + measure_timer_ns -= 1 + for i in range(n_channels): + samples[i, samplesIndex_ns] = waveform[i, pos] + # if (!p->measure_timer_ns) { + if (measure_timer_ns == 0): + index_ns: int = \ + (samplesIndex_ns + samplesLen_ns - measure_len_ns) % samplesLen_ns + meas: float = _measure( + measure_len_ws=measure_len_ws, + samples=samples[i], + spectrum=spectrum[i], + noise_spectrum=noise_spectrum[i], + spectrum_window=spectrum_window, + spectrum_start=spectrum_start, + spectrum_end=spectrum_end, + cepstrum_window=cepstrum_window, + cepstrum_start=cepstrum_start, + cepstrum_end=cepstrum_end, + noise_reduction_amount=noise_reduction_amount, + measure_smooth_time_mult=measure_smooth_time_mult, + noise_up_time_mult=noise_up_time_mult, + noise_down_time_mult=noise_down_time_mult, + index_ns=index_ns, + boot_count=boot_count) + measures[i, measures_index] = meas + mean_meas[i] = mean_meas[i] * trigger_meas_time_mult + meas * (1. - trigger_meas_time_mult) + + has_triggered = has_triggered or (mean_meas[i] >= trigger_level) + if has_triggered: + n: int = measures_len + k: int = measures_index + jTrigger: int = n + jZero: int = n + j: int = 0 + + for j in range(n): + if (measures[i, k] >= trigger_level) and (j <= jTrigger + gap_len): + jZero = jTrigger = j + elif (measures[i, k] == 0) and (jTrigger >= jZero): + jZero = j + k = (k + n - 1) % n + j = min(j, jZero) + # num_measures_to_flush = range_limit(j, num_measures_to_flush, n); + num_measures_to_flush = (min(max(num_measures_to_flush, j), n)) + # end if has_triggered + # end if (measure_timer_ns == 0): + # end for + samplesIndex_ns += 1 + pos += 1 + # end while + if samplesIndex_ns == samplesLen_ns: + samplesIndex_ns = 0 + if measure_timer_ns == 0: + measure_timer_ns = measure_period_ns + measures_index += 1 + measures_index = measures_index % measures_len + if boot_count >= 0: + boot_count = -1 if boot_count == boot_count_max else boot_count + 1 + + if has_triggered: + flushedLen_ns = (measures_len - num_measures_to_flush) * measure_period_ns + samplesIndex_ns = (samplesIndex_ns + flushedLen_ns) % samplesLen_ns + + res = waveform[:, pos - samplesLen_ns + flushedLen_ns:] + # unpack batch + return res.view(shape[:-1] + res.shape[-1:]) diff --git a/torchaudio/kaldi_io.py b/torchaudio/kaldi_io.py index 50b101db7a..d32aaed357 100644 --- a/torchaudio/kaldi_io.py +++ b/torchaudio/kaldi_io.py @@ -1,7 +1,10 @@ # To use this file, the dependency (https://github.com/vesis84/kaldi-io-for-python) # needs to be installed. This is a light wrapper around kaldi_io that returns # torch.Tensors. +from typing import Any, Callable, Iterable, Tuple, Union + import torch +from torch import Tensor from torchaudio.common_utils import IMPORT_KALDI_IO, IMPORT_NUMPY if IMPORT_NUMPY: @@ -20,19 +23,20 @@ ] -def _convert_method_output_to_tensor(file_or_fd, fn, convert_contiguous=False): +def _convert_method_output_to_tensor(file_or_fd: Any, + fn: Callable, + convert_contiguous: bool = False) -> Iterable[Tuple[str, Tensor]]: r"""Takes a method invokes it. The output is converted to a tensor. Args: file_or_fd (str/FileDescriptor): File name or file descriptor - fn (Callable[[...], Generator[str, numpy.ndarray]]): Function that has the signature ( - file name/descriptor) -> Generator(str, numpy.ndarray) and converts it to ( - file name/descriptor) -> Generator(str, torch.Tensor). - convert_contiguous (bool): Determines whether the array should be converted into a - contiguous layout. (Default: ``None``) + fn (Callable): Function that has the signature (file name/descriptor) and converts it to + Iterable[Tuple[str, Tensor]]. + convert_contiguous (bool, optional): Determines whether the array should be converted into a + contiguous layout. (Default: ``False``) Returns: - Generator[str, torch.Tensor]: The string is the key and the tensor is vec/mat + Iterable[Tuple[str, Tensor]]: The string is the key and the tensor is vec/mat """ if not IMPORT_KALDI_IO: raise ImportError('Could not import kaldi_io. Did you install it?') @@ -43,14 +47,14 @@ def _convert_method_output_to_tensor(file_or_fd, fn, convert_contiguous=False): yield key, torch.from_numpy(np_arr) -def read_vec_int_ark(file_or_fd): +def read_vec_int_ark(file_or_fd: Any) -> Iterable[Tuple[str, Tensor]]: r"""Create generator of (key,vector) tuples, which reads from the ark file/stream. Args: file_or_fd (str/FileDescriptor): ark, gzipped ark, pipe or opened file descriptor Returns: - Generator[str, torch.Tensor]: The string is the key and the tensor is the vector read from file + Iterable[Tuple[str, Tensor]]: The string is the key and the tensor is the vector read from file Example >>> # read ark to a 'dictionary' @@ -62,14 +66,14 @@ def read_vec_int_ark(file_or_fd): return _convert_method_output_to_tensor(file_or_fd, kaldi_io.read_vec_int_ark, convert_contiguous=True) -def read_vec_flt_scp(file_or_fd): +def read_vec_flt_scp(file_or_fd: Any) -> Iterable[Tuple[str, Tensor]]: r"""Create generator of (key,vector) tuples, read according to Kaldi scp. Args: file_or_fd (str/FileDescriptor): scp, gzipped scp, pipe or opened file descriptor Returns: - Generator[str, torch.Tensor]: The string is the key and the tensor is the vector read from file + Iterable[Tuple[str, Tensor]]: The string is the key and the tensor is the vector read from file Example >>> # read scp to a 'dictionary' @@ -78,14 +82,14 @@ def read_vec_flt_scp(file_or_fd): return _convert_method_output_to_tensor(file_or_fd, kaldi_io.read_vec_flt_scp) -def read_vec_flt_ark(file_or_fd): +def read_vec_flt_ark(file_or_fd: Any) -> Iterable[Tuple[str, Tensor]]: r"""Create generator of (key,vector) tuples, which reads from the ark file/stream. Args: file_or_fd (str/FileDescriptor): ark, gzipped ark, pipe or opened file descriptor Returns: - Generator[str, torch.Tensor]: The string is the key and the tensor is the vector read from file + Iterable[Tuple[str, Tensor]]: The string is the key and the tensor is the vector read from file Example >>> # read ark to a 'dictionary' @@ -94,14 +98,14 @@ def read_vec_flt_ark(file_or_fd): return _convert_method_output_to_tensor(file_or_fd, kaldi_io.read_vec_flt_ark) -def read_mat_scp(file_or_fd): +def read_mat_scp(file_or_fd: Any) -> Iterable[Tuple[str, Tensor]]: r"""Create generator of (key,matrix) tuples, read according to Kaldi scp. Args: file_or_fd (str/FileDescriptor): scp, gzipped scp, pipe or opened file descriptor Returns: - Generator[str, torch.Tensor]: The string is the key and the tensor is the matrix read from file + Iterable[Tuple[str, Tensor]]: The string is the key and the tensor is the matrix read from file Example >>> # read scp to a 'dictionary' @@ -110,14 +114,14 @@ def read_mat_scp(file_or_fd): return _convert_method_output_to_tensor(file_or_fd, kaldi_io.read_mat_scp) -def read_mat_ark(file_or_fd): +def read_mat_ark(file_or_fd: Any) -> Iterable[Tuple[str, Tensor]]: r"""Create generator of (key,matrix) tuples, which reads from the ark file/stream. Args: file_or_fd (str/FileDescriptor): ark, gzipped ark, pipe or opened file descriptor Returns: - Generator[str, torch.Tensor]: The string is the key and the tensor is the matrix read from file + Iterable[Tuple[str, Tensor]]: The string is the key and the tensor is the matrix read from file Example >>> # read ark to a 'dictionary' diff --git a/torchaudio/sox_effects.py b/torchaudio/sox_effects.py index 76591bd084..472d291902 100644 --- a/torchaudio/sox_effects.py +++ b/torchaudio/sox_effects.py @@ -86,8 +86,8 @@ def __init__(self, out_siginfo: Any = None, out_encinfo: Any = None, filetype: str = "raw") -> None: - self.input_file = None - self.chain = [] + self.input_file: Optional[str] = None + self.chain: List[str] = [] self.MAX_EFFECT_OPTS = 20 self.out_siginfo = out_siginfo self.out_encinfo = out_encinfo @@ -100,12 +100,12 @@ def __init__(self, def append_effect_to_chain(self, ename: str, - eargs: Optional[List[str]] = None) -> None: + eargs: Optional[Union[List[str], str]] = None) -> None: r"""Append effect to a sox effects chain. Args: ename (str): which is the name of effect - eargs (List[str], optional): which is a list of effect options. (Default: ``None``) + eargs (List[str] or str, optional): which is a list of effect options. (Default: ``None``) """ e = SoxEffect() # check if we have a valid effect @@ -149,6 +149,7 @@ def sox_build_flow_effects(self, # print("effect options:", [x.eopts for x in self.chain]) + torchaudio.initialize_sox() import _torch_sox sr = _torch_sox.build_flow_effects(self.input_file, out, diff --git a/torchaudio/transforms.py b/torchaudio/transforms.py index 8775bbdb5b..29da721869 100644 --- a/torchaudio/transforms.py +++ b/torchaudio/transforms.py @@ -26,6 +26,8 @@ 'Fade', 'FrequencyMasking', 'TimeMasking', + 'SlidingWindowCmn', + 'Vad', ] @@ -453,7 +455,7 @@ def __init__(self, super(MFCC, self).__init__() supported_dct_types = [2] if dct_type not in supported_dct_types: - raise ValueError('DCT type not supported'.format(dct_type)) + raise ValueError('DCT type not supported: {}'.format(dct_type)) self.sample_rate = sample_rate self.n_mfcc = n_mfcc self.dct_type = dct_type @@ -837,10 +839,10 @@ class Vol(torch.nn.Module): Args: gain (float): Interpreted according to the given gain_type: - If `gain_typeโ€™ = โ€˜amplitudeโ€™, `gainโ€™ is a positive amplitude ratio. - If `gain_typeโ€™ = โ€˜powerโ€™, `gainโ€™ is a power (voltage squared). - If `gain_typeโ€™ = โ€˜dbโ€™, `gainโ€™ is in decibels. - gain_type (str, optional): Type of gain. One of: โ€˜amplitudeโ€™, โ€˜powerโ€™, โ€˜dbโ€™ (Default: ``"amplitude"``) + If ``gain_type`` = ``amplitude``, ``gain`` is a positive amplitude ratio. + If ``gain_type`` = ``power``, ``gain`` is a power (voltage squared). + If ``gain_type`` = ``db``, ``gain`` is in decibels. + gain_type (str, optional): Type of gain. One of: ``amplitude``, ``power``, ``db`` (Default: ``amplitude``) """ def __init__(self, gain: float, gain_type: str = 'amplitude'): @@ -869,3 +871,157 @@ def forward(self, waveform: Tensor) -> Tensor: waveform = F.gain(waveform, 10 * math.log10(self.gain)) return torch.clamp(waveform, -1, 1) + + +class SlidingWindowCmn(torch.nn.Module): + r""" + Apply sliding-window cepstral mean (and optionally variance) normalization per utterance. + + Args: + cmn_window (int, optional): Window in frames for running average CMN computation (int, default = 600) + min_cmn_window (int, optional): Minimum CMN window used at start of decoding (adds latency only at start). + Only applicable if center == false, ignored if center==true (int, default = 100) + center (bool, optional): If true, use a window centered on the current frame + (to the extent possible, modulo end effects). If false, window is to the left. (bool, default = false) + norm_vars (bool, optional): If true, normalize variance to one. (bool, default = false) + """ + + def __init__(self, + cmn_window: int = 600, + min_cmn_window: int = 100, + center: bool = False, + norm_vars: bool = False) -> None: + super().__init__() + self.cmn_window = cmn_window + self.min_cmn_window = min_cmn_window + self.center = center + self.norm_vars = norm_vars + + def forward(self, waveform: Tensor) -> Tensor: + r""" + Args: + waveform (Tensor): Tensor of audio of dimension (..., time). + + Returns: + Tensor: Tensor of audio of dimension (..., time). + """ + cmn_waveform = F.sliding_window_cmn( + waveform, self.cmn_window, self.min_cmn_window, self.center, self.norm_vars) + return cmn_waveform + + +class Vad(torch.nn.Module): + r"""Voice Activity Detector. Similar to SoX implementation. + Attempts to trim silence and quiet background sounds from the ends of recordings of speech. + The algorithm currently uses a simple cepstral power measurement to detect voice, + so may be fooled by other things, especially music. + + The effect can trim only from the front of the audio, + so in order to trim from the back, the reverse effect must also be used. + + Args: + sample_rate (int): Sample rate of audio signal. + trigger_level (float, optional): The measurement level used to trigger activity detection. + This may need to be cahnged depending on the noise level, signal level, + and other characteristics of the input audio. (Default: 7.0) + trigger_time (float, optional): The time constant (in seconds) + used to help ignore short bursts of sound. (Default: 0.25) + search_time (float, optional): The amount of audio (in seconds) + to search for quieter/shorter bursts of audio to include prior + to the detected trigger point. (Default: 1.0) + allowed_gap (float, optional): The allowed gap (in seconds) between + quiteter/shorter bursts of audio to include prior + to the detected trigger point. (Default: 0.25) + pre_trigger_time (float, optional): The amount of audio (in seconds) to preserve + before the trigger point and any found quieter/shorter bursts. (Default: 0.0) + boot_time (float, optional) The algorithm (internally) uses adaptive noise + estimation/reduction in order to detect the start of the wanted audio. + This option sets the time for the initial noise estimate. (Default: 0.35) + noise_up_time (float, optional) Time constant used by the adaptive noise estimator + for when the noise level is increasing. (Default: 0.1) + noise_down_time (float, optional) Time constant used by the adaptive noise estimator + for when the noise level is decreasing. (Default: 0.01) + noise_reduction_amount (float, optional) Amount of noise reduction to use in + the detection algorithm (e.g. 0, 0.5, ...). (Default: 1.35) + measure_freq (float, optional) Frequency of the algorithmโ€™s + processing/measurements. (Default: 20.0) + measure_duration: (float, optional) Measurement duration. + (Default: Twice the measurement period; i.e. with overlap.) + measure_smooth_time (float, optional) Time constant used to smooth + spectral measurements. (Default: 0.4) + hp_filter_freq (float, optional) "Brick-wall" frequency of high-pass filter applied + at the input to the detector algorithm. (Default: 50.0) + lp_filter_freq (float, optional) "Brick-wall" frequency of low-pass filter applied + at the input to the detector algorithm. (Default: 6000.0) + hp_lifter_freq (float, optional) "Brick-wall" frequency of high-pass lifter used + in the detector algorithm. (Default: 150.0) + lp_lifter_freq (float, optional) "Brick-wall" frequency of low-pass lifter used + in the detector algorithm. (Default: 2000.0) + + References: + http://sox.sourceforge.net/sox.html + """ + + def __init__(self, + sample_rate: int, + trigger_level: float = 7.0, + trigger_time: float = 0.25, + search_time: float = 1.0, + allowed_gap: float = 0.25, + pre_trigger_time: float = 0.0, + boot_time: float = .35, + noise_up_time: float = .1, + noise_down_time: float = .01, + noise_reduction_amount: float = 1.35, + measure_freq: float = 20.0, + measure_duration: Optional[float] = None, + measure_smooth_time: float = .4, + hp_filter_freq: float = 50., + lp_filter_freq: float = 6000., + hp_lifter_freq: float = 150., + lp_lifter_freq: float = 2000.) -> None: + super().__init__() + + self.sample_rate = sample_rate + self.trigger_level = trigger_level + self.trigger_time = trigger_time + self.search_time = search_time + self.allowed_gap = allowed_gap + self.pre_trigger_time = pre_trigger_time + self.boot_time = boot_time + self.noise_up_time = noise_up_time + self.noise_down_time = noise_up_time + self.noise_reduction_amount = noise_reduction_amount + self.measure_freq = measure_freq + self.measure_duration = measure_duration + self.measure_smooth_time = measure_smooth_time + self.hp_filter_freq = hp_filter_freq + self.lp_filter_freq = lp_filter_freq + self.hp_lifter_freq = hp_lifter_freq + self.lp_lifter_freq = lp_lifter_freq + + def forward(self, waveform: Tensor) -> Tensor: + r""" + Args: + waveform (Tensor): Tensor of audio of dimension `(..., time)` + """ + return F.vad( + waveform=waveform, + sample_rate=self.sample_rate, + trigger_level=self.trigger_level, + trigger_time=self.trigger_time, + search_time=self.search_time, + allowed_gap=self.allowed_gap, + pre_trigger_time=self.pre_trigger_time, + boot_time=self.boot_time, + noise_up_time=self.noise_up_time, + noise_down_time=self.noise_up_time, + noise_reduction_amount=self.noise_reduction_amount, + measure_freq=self.measure_freq, + measure_duration=self.measure_duration, + measure_smooth_time=self.measure_smooth_time, + hp_filter_freq=self.hp_filter_freq, + lp_filter_freq=self.lp_filter_freq, + hp_lifter_freq=self.hp_lifter_freq, + lp_lifter_freq=self.lp_lifter_freq, + ) From e065a0f4c49680b17e2cfd9e666962a8ee1cefb0 Mon Sep 17 00:00:00 2001 From: Moto Hira Date: Thu, 28 May 2020 12:18:22 -0700 Subject: [PATCH 05/19] Import torchaudio 20200528 Summary: Import Up to #664 Reviewed By: cpuhrsch Differential Revision: D21728204 fbshipit-source-id: 648dd622087fa762194ca5f89a310500e777263d --- .use_external_sox | 0 README.md | 25 +- build_tools/__init__.py | 0 build_tools/setup_helpers/__init__.py | 1 + .../setup_helpers/build_third_party.sh | 36 ++ .../setup_helpers/build_third_party_helper.sh | 173 +++++++++ build_tools/setup_helpers/extension.py | 111 ++++++ docs/source/functional.rst | 5 + docs/source/models.rst | 17 + packaging/build_from_source.sh | 62 ---- packaging/build_wheel.sh | 8 +- packaging/pkg_helpers.bash | 2 +- packaging/torchaudio/build.sh | 4 +- setup.py | 99 ++---- test/README.md | 51 +++ test/__init__.py | 10 + test/assets/vad-go-mono-32000.wav | Bin 0 -> 54660 bytes test/assets/vad-go-stereo-44100.wav | Bin 0 -> 173400 bytes test/assets/vad-hello-mono-32000.wav | Bin 275106 -> 0 bytes test/assets/vad-hello-stereo-44100.wav | Bin 937862 -> 0 bytes test/common_utils.py | 104 ++++++ test/kaldi_compatibility_cpu_test.py | 5 + test/kaldi_compatibility_cuda_test.py | 5 + test/kaldi_compatibility_impl.py | 104 ++++++ test/test_batch_consistency.py | 102 +++--- test/test_compliance_kaldi.py | 10 +- test/test_dataloader.py | 10 +- test/test_datasets.py | 5 +- test/test_functional.py | 39 +-- test/test_io.py | 4 +- test/test_kaldi_io.py | 2 +- test/test_librosa_compatibility.py | 184 +++++----- test/test_models.py | 30 ++ test/test_sox_compatibility.py | 93 +++-- test/test_sox_effects.py | 50 ++- test/test_transforms.py | 17 +- test/torchscript_consistency_cpu_test.py | 5 + test/torchscript_consistency_cuda_test.py | 5 + ...ncy.py => torchscript_consistency_impl.py} | 221 ++++++------ torchaudio/__init__.py | 40 +-- torchaudio/_sox_backend.py | 8 +- torchaudio/compliance/kaldi.py | 67 ++-- torchaudio/{torch_sox.cpp => csrc/sox.cpp} | 14 +- torchaudio/{torch_sox.h => csrc/sox.h} | 18 +- torchaudio/functional.py | 327 +++++++++++------- torchaudio/models/__init__.py | 1 + torchaudio/models/wav2letter.py | 74 ++++ torchaudio/sox_effects.py | 26 +- torchaudio/transforms.py | 30 +- 49 files changed, 1517 insertions(+), 687 deletions(-) create mode 100644 .use_external_sox create mode 100644 build_tools/__init__.py create mode 100644 build_tools/setup_helpers/__init__.py create mode 100755 build_tools/setup_helpers/build_third_party.sh create mode 100644 build_tools/setup_helpers/build_third_party_helper.sh create mode 100644 build_tools/setup_helpers/extension.py create mode 100644 docs/source/models.rst delete mode 100755 packaging/build_from_source.sh create mode 100644 test/README.md create mode 100644 test/__init__.py create mode 100644 test/assets/vad-go-mono-32000.wav create mode 100644 test/assets/vad-go-stereo-44100.wav delete mode 100644 test/assets/vad-hello-mono-32000.wav delete mode 100644 test/assets/vad-hello-stereo-44100.wav create mode 100644 test/kaldi_compatibility_cpu_test.py create mode 100644 test/kaldi_compatibility_cuda_test.py create mode 100644 test/kaldi_compatibility_impl.py create mode 100644 test/test_models.py create mode 100644 test/torchscript_consistency_cpu_test.py create mode 100644 test/torchscript_consistency_cuda_test.py rename test/{test_torchscript_consistency.py => torchscript_consistency_impl.py} (74%) rename torchaudio/{torch_sox.cpp => csrc/sox.cpp} (98%) rename torchaudio/{torch_sox.h => csrc/sox.h} (88%) create mode 100644 torchaudio/models/__init__.py create mode 100644 torchaudio/models/wav2letter.py diff --git a/.use_external_sox b/.use_external_sox new file mode 100644 index 0000000000..e69de29bb2 diff --git a/README.md b/README.md index 816fd75f6c..fc3326075a 100644 --- a/README.md +++ b/README.md @@ -13,7 +13,7 @@ having all the computations be through Pytorch operations which makes it easy to use and feel like a natural extension. - [Support audio I/O (Load files, Save files)](http://pytorch.org/audio/) - - Load the following formats into a torch Tensor using sox + - Load the following formats into a torch Tensor using SoX - mp3, wav, aac, ogg, flac, avr, cdda, cvs/vms, - aiff, au, amr, mp2, mp4, ac3, avi, wmv, - mpeg, ircam and any other format supported by libsox. @@ -98,10 +98,29 @@ Quick Usage ```python import torchaudio -waveform, sample_rate = torchaudio.load('foo.mp3') # load tensor from file -torchaudio.save('foo_save.mp3', waveform, sample_rate) # save tensor to file + +waveform, sample_rate = torchaudio.load('foo.wav') # load tensor from file +torchaudio.save('foo_save.wav', waveform, sample_rate) # save tensor to file ``` +Backend Dispatch +---------------- + +By default in OSX and Linux, torchaudio uses SoX as a backend to load and save files. +The backend can be changed to [SoundFile](https://pysoundfile.readthedocs.io/en/latest/) +using the following. See [SoundFile](https://pysoundfile.readthedocs.io/en/latest/) +for installation instructions. + +```python +import torchaudio +torchaudio.set_audio_backend("soundfile") # switch backend + +waveform, sample_rate = torchaudio.load('foo.wav') # load tensor from file, as usual +torchaudio.save('foo_save.wav', waveform, sample_rate) # save tensor to file, as usual +``` + +Unlike SoX, SoundFile does not currently support mp3. + API Reference ------------- diff --git a/build_tools/__init__.py b/build_tools/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/build_tools/setup_helpers/__init__.py b/build_tools/setup_helpers/__init__.py new file mode 100644 index 0000000000..7afa3f31cd --- /dev/null +++ b/build_tools/setup_helpers/__init__.py @@ -0,0 +1 @@ +from .extension import * # noqa diff --git a/build_tools/setup_helpers/build_third_party.sh b/build_tools/setup_helpers/build_third_party.sh new file mode 100755 index 0000000000..c5e8f8115d --- /dev/null +++ b/build_tools/setup_helpers/build_third_party.sh @@ -0,0 +1,36 @@ +#!/bin/bash +# Build third party libraries in `/third_party/build` or in `/third_party/build`. + +set -e + +this_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" +root_dir="${this_dir}/../.." + +prefix="${1:-}" +if [ -z "${prefix}" ]; then + prefix="${root_dir}" +fi + +tp_dir="${prefix}/third_party" +tmp_dir="${tp_dir}/tmp" +build_dir="${tp_dir}/build" + +mkdir -p "${tmp_dir}" "${build_dir}" + +. "${this_dir}/build_third_party_helper.sh" + +if ! found_lame "${build_dir}" ; then + build_lame "${tmp_dir}" "${build_dir}" +fi + +if ! found_flac "${build_dir}" ; then + build_flac "${tmp_dir}" "${build_dir}" +fi + +if ! found_mad "${build_dir}" ; then + build_mad "${tmp_dir}" "${build_dir}" +fi + +if ! found_sox "${build_dir}" ; then + build_sox "${tmp_dir}" "${build_dir}" +fi diff --git a/build_tools/setup_helpers/build_third_party_helper.sh b/build_tools/setup_helpers/build_third_party_helper.sh new file mode 100644 index 0000000000..a31fafe718 --- /dev/null +++ b/build_tools/setup_helpers/build_third_party_helper.sh @@ -0,0 +1,173 @@ +#!/usr/bin/env bash + +set -euo pipefail + +# Global options +CURL_OPTS="-L --retry 10 --connect-timeout 5 --max-time 180" +MAKE_OPTS="-j" +CONFIG_OPTS="" + +if [ -z ${DEBUG+x} ]; then + CURL_OPTS="${CURL_OPTS} --silent --show-error" + MAKE_OPTS="${MAKE_OPTS} --quiet" + CONFIG_OPTS="${CONFIG_OPTS} --quiet" +fi + +all_found() { + dir="$1" + shift + while [ "$#" -gt 0 ]; do + if [ ! -f "${dir}/$1" ]; then + return 1 + fi + shift + done +} + + +found_lame() { + all_found "$1" 'include/lame/lame.h' 'lib/libmp3lame.a' +} + +found_flac() { + all_found "$1" \ + 'include/FLAC/format.h' \ + 'include/FLAC/stream_decoder.h' \ + 'include/FLAC/export.h' \ + 'include/FLAC/ordinals.h' \ + 'include/FLAC/all.h' \ + 'include/FLAC/assert.h' \ + 'include/FLAC/callback.h' \ + 'include/FLAC/metadata.h' \ + 'include/FLAC/stream_encoder.h' \ + 'include/FLAC++/export.h' \ + 'include/FLAC++/decoder.h' \ + 'include/FLAC++/all.h' \ + 'include/FLAC++/metadata.h' \ + 'include/FLAC++/encoder.h' \ + 'lib/libFLAC++.a' \ + 'lib/libFLAC.a' +} + +found_mad() { + all_found "$1" 'include/mad.h' 'lib/libmad.a' +} + +found_sox() { + all_found "$1" 'include/sox.h' 'lib/libsox.a' +} + +build_lame() { + work_dir="$1" + install_dir="$2" + package="lame-3.99.5" + url="https://downloads.sourceforge.net/project/lame/lame/3.99/lame-3.99.5.tar.gz" + ( + cd "${work_dir}" + if [ ! -d "${package}" ]; then + if [ ! -f "${package}.tar.gz" ]; then + printf "Fetching liblame from %s\n" "${url}" + curl $CURL_OPTS -o "${package}.tar.gz" "${url}" + fi + tar xfp "${package}.tar.gz" + fi + # build statically + printf "Building liblame\n" + cd "${package}" + if [ ! -f Makefile ]; then + ./configure ${CONFIG_OPTS} \ + --disable-shared --enable-static --prefix="${install_dir}" CFLAGS=-fPIC CXXFLAGS=-fPIC \ + --with-pic --disable-debug --disable-dependency-tracking --enable-nasm + fi + make ${MAKE_OPTS} > make.log 2>&1 + make ${MAKE_OPTS} install + ) +} + +build_flac() { + work_dir="$1" + install_dir="$2" + package="flac-1.3.2" + url="https://downloads.sourceforge.net/project/flac/flac-src/flac-1.3.2.tar.xz" + ( + cd "${work_dir}" + if [ ! -d "${package}" ]; then + if [ ! -f "${package}.tar.xz" ]; then + printf "Fetching flac from %s\n" "${url}" + curl $CURL_OPTS -o "${package}.tar.xz" "${url}" + fi + tar xfp "${package}.tar.xz" + fi + # build statically + printf "Building flac\n" + cd "${package}" + if [ ! -f Makefile ]; then + ./configure ${CONFIG_OPTS} \ + --disable-shared --enable-static --prefix="${install_dir}" CFLAGS=-fPIC CXXFLAGS=-fPIC \ + --with-pic --disable-debug --disable-dependency-tracking + fi + make ${MAKE_OPTS} > make.log 2>&1 + make ${MAKE_OPTS} install + ) +} + +build_mad() { + work_dir="$1" + install_dir="$2" + package="libmad-0.15.1b" + url="https://downloads.sourceforge.net/project/mad/libmad/0.15.1b/libmad-0.15.1b.tar.gz" + ( + cd "${work_dir}" + if [ ! -d "${package}" ]; then + if [ ! -f "${package}.tar.gz" ]; then + printf "Fetching mad from %s\n" "${url}" + curl $CURL_OPTS -o "${package}.tar.gz" "${url}" + fi + tar xfp "${package}.tar.gz" + fi + # build statically + printf "Building mad\n" + cd "${package}" + if [ ! -f Makefile ]; then + # See https://stackoverflow.com/a/12864879/23845 + sed -i.bak 's/-march=i486//' configure + ./configure ${CONFIG_OPTS} \ + --disable-shared --enable-static --prefix="${install_dir}" CFLAGS=-fPIC CXXFLAGS=-fPIC \ + --with-pic --disable-debug --disable-dependency-tracking + fi + make ${MAKE_OPTS} > make.log 2>&1 + make ${MAKE_OPTS} install + ) +} + +build_sox() { + work_dir="$1" + install_dir="$2" + package="sox-14.4.2" + url="https://downloads.sourceforge.net/project/sox/sox/14.4.2/sox-14.4.2.tar.bz2" + ( + cd "${work_dir}" + if [ ! -d "${package}" ]; then + if [ ! -f "${package}.tar.bz2" ]; then + printf "Fetching SoX from %s\n" "${url}" + curl $CURL_OPTS -o "${package}.tar.bz2" "${url}" + fi + tar xfp "${package}.tar.bz2" + fi + # build statically + printf "Building sox\n" + cd "${package}" + if [ ! -f Makefile ]; then + # --without-png makes OS X build less hazardous; somehow the build + # finds png and enables it. We don't want it; we'd need to package + # it statically if we do. + ./configure ${CONFIG_OPTS} --disable-shared --enable-static --prefix="${install_dir}" \ + LDFLAGS="-L${install_dir}/lib" CPPFLAGS="-I${install_dir}/include" \ + --with-lame --with-flac --with-mad --without-alsa --without-coreaudio \ + --without-png --without-oggvorbis --without-oss --without-sndfile \ + CFLAGS=-fPIC CXXFLAGS=-fPIC --with-pic --disable-debug --disable-dependency-tracking + fi + make ${MAKE_OPTS} > make.log 2>&1 + make ${MAKE_OPTS} install + ) +} diff --git a/build_tools/setup_helpers/extension.py b/build_tools/setup_helpers/extension.py new file mode 100644 index 0000000000..65e48dfc10 --- /dev/null +++ b/build_tools/setup_helpers/extension.py @@ -0,0 +1,111 @@ +import platform +import subprocess +from pathlib import Path + +from torch.utils.cpp_extension import ( + CppExtension, + BuildExtension as TorchBuildExtension +) + +__all__ = [ + 'get_ext_modules', + 'BuildExtension', +] + +_THIS_DIR = Path(__file__).parent.resolve() +_ROOT_DIR = _THIS_DIR.parent.parent.resolve() +_CSRC_DIR = _ROOT_DIR / 'torchaudio' / 'csrc' +_TP_BASE_DIR = _ROOT_DIR / 'third_party' +_TP_INSTALL_DIR = _TP_BASE_DIR / 'build' + +# Temporary fix for building in fbcode +# at the moment, we have to use external sox in fbcode +_BUILD_DEPS = not (_ROOT_DIR / '.use_external_sox').exists() + + +def _get_eca(debug): + eca = [] + if debug: + eca += ["-O0", "-g"] + else: + eca += ["-O3"] + return eca + + +def _get_ela(debug): + ela = [] + if debug: + if platform.system() == "Windows": + ela += ["/DEBUG:FULL"] + else: + ela += ["-O0", "-g"] + else: + ela += ["-O3"] + return ela + + +def _get_srcs(): + return [str(p) for p in _CSRC_DIR.glob('**/*.cpp')] + + +def _get_include_dirs(): + dirs = [ + str(_ROOT_DIR), + ] + if _BUILD_DEPS: + dirs.append(str(_TP_INSTALL_DIR / 'include')) + return dirs + + +def _get_extra_objects(): + objs = [] + if _BUILD_DEPS: + # NOTE: The order of the library listed bellow matters. + # + # (the most important thing is that dependencies come after a library + # e.g., sox comes first) + libs = ['libsox.a', 'libmad.a', 'libFLAC.a', 'libmp3lame.a'] + for lib in libs: + objs.append(str(_TP_INSTALL_DIR / 'lib' / lib)) + return objs + + +def _get_libraries(): + return [] if _BUILD_DEPS else ['sox'] + + +def _build_codecs(): + subprocess.run( + args=[str(_THIS_DIR / 'build_third_party.sh')], + check=True, + ) + + +def _configure_third_party(): + _build_codecs() + + +_EXT_NAME = 'torchaudio._torchaudio' + + +def get_ext_modules(debug=False): + if platform.system() == 'Windows': + return None + return [ + CppExtension( + _EXT_NAME, + _get_srcs(), + libraries=_get_libraries(), + include_dirs=_get_include_dirs(), + extra_compile_args=_get_eca(debug), + extra_objects=_get_extra_objects(), + extra_link_args=_get_ela(debug), + ), + ] + + +class BuildExtension(TorchBuildExtension): + def build_extension(self, ext): + if ext.name == _EXT_NAME and _BUILD_DEPS: + _configure_third_party() + super().build_extension(ext) diff --git a/docs/source/functional.rst b/docs/source/functional.rst index d8f318ea18..5f68666539 100644 --- a/docs/source/functional.rst +++ b/docs/source/functional.rst @@ -138,6 +138,11 @@ Functions to perform common audio operations. .. autofunction:: overdrive +:hidden:`phaser` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autofunction:: phaser + :hidden:`mask_along_axis` ~~~~~~~~~~~~~~~~~~~~~~~~~ diff --git a/docs/source/models.rst b/docs/source/models.rst new file mode 100644 index 0000000000..5b40482976 --- /dev/null +++ b/docs/source/models.rst @@ -0,0 +1,17 @@ +.. role:: hidden + :class: hidden-section + +torchaudio.models +====================== + +.. currentmodule:: torchaudio.models + +The models subpackage contains definitions of models for addressing common audio tasks. + + +:hidden:`Wav2Letter` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: Wav2Letter + + .. automethod:: forward diff --git a/packaging/build_from_source.sh b/packaging/build_from_source.sh deleted file mode 100755 index f5981f0a5e..0000000000 --- a/packaging/build_from_source.sh +++ /dev/null @@ -1,62 +0,0 @@ -#!/bin/bash - -set -ex - -# Arguments: PREFIX, specifying where to install dependencies into - -PREFIX="$1" - -rm -rf /tmp/torchaudio-deps -mkdir /tmp/torchaudio-deps -pushd /tmp/torchaudio-deps - - -# Curl Settings -CURL_OPTS="-L --retry 10 --connect-timeout 5 --max-time 180" - -curl $CURL_OPTS -o sox-14.4.2.tar.bz2 "https://downloads.sourceforge.net/project/sox/sox/14.4.2/sox-14.4.2.tar.bz2" -curl $CURL_OPTS -o lame-3.99.5.tar.gz "https://downloads.sourceforge.net/project/lame/lame/3.99/lame-3.99.5.tar.gz" -curl $CURL_OPTS -o flac-1.3.2.tar.xz "https://downloads.sourceforge.net/project/flac/flac-src/flac-1.3.2.tar.xz" -curl $CURL_OPTS -o libmad-0.15.1b.tar.gz "https://downloads.sourceforge.net/project/mad/libmad/0.15.1b/libmad-0.15.1b.tar.gz" - -# unpack the dependencies -tar xfp sox-14.4.2.tar.bz2 -tar xfp lame-3.99.5.tar.gz -tar xfp flac-1.3.2.tar.xz -tar xfp libmad-0.15.1b.tar.gz - -# build lame, statically -pushd lame-3.99.5 -./configure --disable-shared --enable-static --prefix="$PREFIX/third_party/lame" CFLAGS=-fPIC CXXFLAGS=-fPIC --with-pic --disable-debug --disable-dependency-tracking --enable-nasm -make -s -j && make install -popd - -# build flac, statically -pushd flac-1.3.2 -./configure --disable-shared --enable-static --prefix="$PREFIX/third_party/flac" CFLAGS=-fPIC CXXFLAGS=-fPIC \ - --with-pic --disable-debug --disable-dependency-tracking -make -s -j && make install -popd - -# build mad, statically -pushd libmad-0.15.1b -# See https://stackoverflow.com/a/12864879/23845 -sed -i.bak 's/-march=i486//' configure -./configure --disable-shared --enable-static --prefix="$PREFIX/third_party/mad" CFLAGS=-fPIC CXXFLAGS=-fPIC \ - --with-pic --disable-debug --disable-dependency-tracking -make -s -j && make install -popd - -# build sox, statically -# --without-png makes OS X build less hazardous; somehow the build -# finds png and enables it. We don't want it; we'd need to package -# it statically if we do. -pushd sox-14.4.2 -./configure --disable-shared --enable-static --prefix="$PREFIX/third_party/sox" \ - LDFLAGS="-L$PREFIX/third_party/lame/lib -L$PREFIX/third_party/flac/lib -L$PREFIX/third_party/mad/lib" \ - CPPFLAGS="-I$PREFIX/third_party/lame/include -I$PREFIX/third_party/flac/include -I$PREFIX/third_party/mad/include" \ - --with-lame --with-flac --with-mad --without-alsa --without-coreaudio --without-png --without-oggvorbis --without-oss --without-sndfile CFLAGS=-fPIC CXXFLAGS=-fPIC --with-pic --disable-debug --disable-dependency-tracking -make -s -j && make install -popd - -popd diff --git a/packaging/build_wheel.sh b/packaging/build_wheel.sh index fc0742ece1..73cec89ff5 100755 --- a/packaging/build_wheel.sh +++ b/packaging/build_wheel.sh @@ -8,8 +8,12 @@ export BUILD_TYPE="wheel" export NO_CUDA_PACKAGE=1 setup_env 0.6.0 setup_wheel_python -"$script_dir/build_from_source.sh" "$(pwd)" # Build static dependencies pip_install numpy future setup_pip_pytorch_version python setup.py clean -IS_WHEEL=1 python setup.py bdist_wheel +if [[ "$OSTYPE" == "msys" ]]; then + python_tag="$(echo "cp$PYTHON_VERSION" | tr -d '.')" + python setup.py bdist_wheel --plat-name win_amd64 --python-tag $python_tag +else + python setup.py bdist_wheel +fi diff --git a/packaging/pkg_helpers.bash b/packaging/pkg_helpers.bash index bc8ec997df..4030128a98 100644 --- a/packaging/pkg_helpers.bash +++ b/packaging/pkg_helpers.bash @@ -122,7 +122,7 @@ retry () { # # Precondition: If Linux, you are in a soumith/manylinux-cuda* Docker image setup_wheel_python() { - if [[ "$(uname)" == Darwin ]]; then + if [[ "$(uname)" == Darwin || "$OSTYPE" == "msys" ]]; then eval "$(conda shell.bash hook)" conda env remove -n "env$PYTHON_VERSION" || true conda create -yn "env$PYTHON_VERSION" python="$PYTHON_VERSION" diff --git a/packaging/torchaudio/build.sh b/packaging/torchaudio/build.sh index dff89cf31b..e2b53ddcf4 100644 --- a/packaging/torchaudio/build.sh +++ b/packaging/torchaudio/build.sh @@ -1,6 +1,4 @@ #!/usr/bin/env bash set -ex -packaging/build_from_source.sh "$(pwd)" - -IS_CONDA=1 python setup.py install --single-version-externally-managed --record=record.txt +python setup.py install --single-version-externally-managed --record=record.txt diff --git a/setup.py b/setup.py index 0128a2fe61..5cf1ad680a 100644 --- a/setup.py +++ b/setup.py @@ -1,65 +1,22 @@ #!/usr/bin/env python import os -import platform -import sys +import shutil import subprocess - +from pathlib import Path +import distutils.command.clean from setuptools import setup, find_packages -from torch.utils.cpp_extension import BuildExtension, CppExtension - - -def check_env_flag(name, default=''): - return os.getenv(name, default).upper() in set(['ON', '1', 'YES', 'TRUE', 'Y']) - -DEBUG = check_env_flag('DEBUG') -IS_WHEEL = check_env_flag('IS_WHEEL') -IS_CONDA = check_env_flag('IS_CONDA') - -print('DEBUG:', DEBUG, 'IS_WHEEL:', IS_WHEEL, 'IS_CONDA:', IS_CONDA) - -eca = [] -ela = [] -if DEBUG: - if platform.system() == 'Windows': - ela += ['/DEBUG:FULL'] - else: - eca += ['-O0', '-g'] - ela += ['-O0', '-g'] +from build_tools import setup_helpers -libraries = [] -include_dirs = [] -extra_objects = [] - -# Hypothetically, the conda distribution could rely on an external sox, -# but the library is pretty small and it is not available on the default -# anaconda channel. So we statically link it in, just as we do with wheels. -if IS_WHEEL or IS_CONDA: - audio_path = os.path.dirname(os.path.abspath(__file__)) - - include_dirs += [os.path.join(audio_path, 'third_party/flac/include')] - include_dirs += [os.path.join(audio_path, 'third_party/lame/include')] - include_dirs += [os.path.join(audio_path, 'third_party/sox/include')] - include_dirs += [os.path.join(audio_path, 'third_party/mad/include')] - - # proper link order (sox, mad, flac, lame) - # (the most important thing is that dependencies come after a libraryl - # e.g., sox comes first) - extra_objects += [os.path.join(audio_path, 'third_party/sox/lib/libsox.a')] - extra_objects += [os.path.join(audio_path, 'third_party/mad/lib/libmad.a')] - extra_objects += [os.path.join(audio_path, 'third_party/flac/lib/libFLAC.a')] - extra_objects += [os.path.join(audio_path, 'third_party/lame/lib/libmp3lame.a')] -else: - libraries += ['sox'] +ROOT_DIR = Path(__file__).parent.resolve() # Creating the version file -cwd = os.path.dirname(os.path.abspath(__file__)) version = '0.6.0a0' sha = 'Unknown' try: - sha = subprocess.check_output(['git', 'rev-parse', 'HEAD'], cwd=cwd).decode('ascii').strip() + sha = subprocess.check_output(['git', 'rev-parse', 'HEAD'], cwd=ROOT_DIR).decode('ascii').strip() except Exception: pass @@ -69,7 +26,7 @@ def check_env_flag(name, default=''): version += '+' + sha[:7] print('-- Building version ' + version) -version_path = os.path.join(cwd, 'torchaudio', 'version.py') +version_path = ROOT_DIR / 'torchaudio' / 'version.py' with open(version_path, 'w') as f: f.write("__version__ = '{}'\n".format(version)) f.write("git_version = {}\n".format(repr(sha))) @@ -80,19 +37,26 @@ def check_env_flag(name, default=''): if pytorch_package_version is not None: pytorch_package_dep += "==" + pytorch_package_version -if platform.system() == 'Windows': - ext_modules = None -else: - ext_modules = [ - CppExtension( - '_torch_sox', - ['torchaudio/torch_sox.cpp'], - libraries=libraries, - include_dirs=include_dirs, - extra_compile_args=eca, - extra_objects=extra_objects, - extra_link_args=ela), - ] + +class clean(distutils.command.clean.clean): + def run(self): + # Run default behavior first + distutils.command.clean.clean.run(self) + + # Remove torchaudio extension + for path in (ROOT_DIR / 'torchaudio').glob('**/*.so'): + print(f'removing \'{path}\'') + path.unlink() + # Remove build directory + build_dirs = [ + ROOT_DIR / 'build', + ROOT_DIR / 'third_party' / 'build', + ] + for path in build_dirs: + if path.exists(): + print(f'removing \'{path}\' (and everything under it)') + shutil.rmtree(str(path), ignore_errors=True) + setup( name="torchaudio", @@ -116,9 +80,10 @@ def check_env_flag(name, default=''): "Topic :: Multimedia :: Sound/Audio", "Topic :: Scientific/Engineering :: Artificial Intelligence" ], - # Exclude the build files. - packages=find_packages(exclude=["build"]), - ext_modules=ext_modules, - cmdclass={'build_ext': BuildExtension}, + packages=find_packages(exclude=["build*", "test*", "torchaudio.csrc*", "third_party*", "build_tools*"]), + ext_modules=setup_helpers.get_ext_modules(), + cmdclass={ + 'build_ext': setup_helpers.BuildExtension.with_options(no_python_abi_suffix=True) + }, install_requires=[pytorch_package_dep] ) diff --git a/test/README.md b/test/README.md new file mode 100644 index 0000000000..a6686603af --- /dev/null +++ b/test/README.md @@ -0,0 +1,51 @@ +# Torchaudio Test Suite + +## Structure of tests + +The following is an overview of the tests and related modules for `torchaudio`. + +### Purpose specific test suites + +#### Numerical compatibility agains existing software +- [Librosa compatibility test](./test_librosa_compatibility.py) + Test suite for numerical compatibility against librosa. +- [SoX compatibility test](./test_sox_compatibility.py) + Test suite for numerical compatibility against SoX. +- [Kaldi compatibility test](./test_kaldi_compatibility.py) + Test suite for numerical compatibility against Kaldi. + +#### Result consistency with PyTorch framework +- [TorchScript consistency test](./test_torchscript_consistency.py) + Test suite to check 1. if an API is TorchScript-able, and 2. the results from Python and Torchscript match. +- [Batch consistency test](./test_batch_consistency.py) + Test suite to check if functionals/Transforms handle single sample input and batch input and return the same result. + +### Module specific test suites + +The following test modules are defined for corresponding `torchaudio` module/functions. + +- [`torchaudio.datasets`](./test_datasets.py) +- [`torchaudio.functional`](./test_functional.py) +- [`torchaudio.transforms`](./test_transforms.py) +- [`torchaudio.compliance.kaldi`](./test_compliance_kaldi.py) +- [`torchaudio.kaldi_io`](./test_kaldi_io.py) +- [`torchaudio.sox_effects`](test/test_sox_effects.py) +- [`torchaudio.save`, `torchaudio.load`, `torchaudio.info`](test/test_io.py) + +### Test modules that do not fall into the above categories +- [test_dataloader.py](./test_dataloader.py) + Simple test for loading data and applying preprocessing. + +### Support files +- [assets](./assets): Contain sample audio files. +- [assets/kaldi](./assets/kaldi): Contains Kaldi format matrix files used in [./test_compliance_kaldi.py](./test_compliance_kaldi.py). +- [compliance](./compliance): Scripts used to generate above Kaldi matrix files. + + +## Adding test + +When you add a new feature(functional/transform), consider the following + +1. When you add a new feature, please make it Torchscript-able and batch-consistent unless it degrades the performance. Please add the tests to see if the new feature meet these requirements. +1. If the feature should be numerical compatible against existing software (SoX, Librosa, Kaldi etc), add a corresponding test. +1. 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b/test/common_utils.py index d3ef58a51f..5d1dc8abe1 100644 --- a/test/common_utils.py +++ b/test/common_utils.py @@ -1,9 +1,12 @@ import os import tempfile +import unittest +from typing import Type, Iterable, Union from contextlib import contextmanager from shutil import copytree import torch +from torch.testing._internal.common_utils import TestCase import torchaudio _TEST_DIR_PATH = os.path.dirname(os.path.realpath(__file__)) @@ -78,3 +81,104 @@ def supports_mp3(backend): BACKENDS_MP3 = filter_backends_with_mp3(BACKENDS) + + +class TestBaseMixin: + dtype = None + device = None + + +_SKIP_IF_NO_CUDA = unittest.skipIf(not torch.cuda.is_available(), reason='CUDA not available') + + +def define_test_suite(testbase: Type[TestBaseMixin], dtype: str, device: str): + if dtype not in ['float32', 'float64']: + raise NotImplementedError(f'Unexpected dtype: {dtype}') + if device not in ['cpu', 'cuda']: + raise NotImplementedError(f'Unexpected device: {device}') + + name = f'Test{testbase.__name__}_{device.upper()}_{dtype.capitalize()}' + attrs = {'dtype': getattr(torch, dtype), 'device': torch.device(device)} + testsuite = type(name, (testbase, TestCase), attrs) + + if device == 'cuda': + testsuite = _SKIP_IF_NO_CUDA(testsuite) + return testsuite + + +def define_test_suites( + scope: dict, + testbases: Iterable[Type[TestBaseMixin]], + dtypes: Iterable[str] = ('float32', 'float64'), + devices: Iterable[str] = ('cpu', 'cuda'), +): + for suite in testbases: + for device in devices: + for dtype in dtypes: + t = define_test_suite(suite, dtype, device) + scope[t.__name__] = t + + +def get_whitenoise( + *, + sample_rate: int = 16000, + duration: float = 1, # seconds + n_channels: int = 1, + seed: int = 0, + dtype: Union[str, torch.dtype] = "float32", + device: Union[str, torch.device] = "cpu", +): + """Generate pseudo audio data with whitenoise + + Args: + sample_rate: Sampling rate + duration: Length of the resulting Tensor in seconds. + n_channels: Number of channels + seed: Seed value used for random number generation. + Note that this function does not modify global random generator state. + dtype: Torch dtype + device: device + Returns: + Tensor: shape of (n_channels, sample_rate * duration) + """ + if isinstance(dtype, str): + dtype = getattr(torch, dtype) + shape = [n_channels, sample_rate * duration] + # According to the doc, folking rng on all CUDA devices is slow when there are many CUDA devices, + # so we only folk on CPU, generate values and move the data to the given device + with torch.random.fork_rng([]): + torch.random.manual_seed(seed) + tensor = torch.randn(shape, dtype=dtype, device='cpu') + tensor /= 2.0 + tensor.clamp_(-1.0, 1.0) + return tensor.to(device=device) + + +def get_sinusoid( + *, + frequency: float = 300, + sample_rate: int = 16000, + duration: float = 1, # seconds + n_channels: int = 1, + dtype: Union[str, torch.dtype] = "float32", + device: Union[str, torch.device] = "cpu", +): + """Generate pseudo audio data with sine wave. + + Args: + frequency: Frequency of sine wave + sample_rate: Sampling rate + duration: Length of the resulting Tensor in seconds. + n_channels: Number of channels + dtype: Torch dtype + device: device + + Returns: + Tensor: shape of (n_channels, sample_rate * duration) + """ + if isinstance(dtype, str): + dtype = getattr(torch, dtype) + pie2 = 2 * 3.141592653589793 + end = pie2 * frequency * duration + theta = torch.linspace(0, end, sample_rate * duration, dtype=dtype, device=device) + return torch.sin(theta, out=None).repeat([n_channels, 1]) diff --git a/test/kaldi_compatibility_cpu_test.py b/test/kaldi_compatibility_cpu_test.py new file mode 100644 index 0000000000..cd85074ac0 --- /dev/null +++ b/test/kaldi_compatibility_cpu_test.py @@ -0,0 +1,5 @@ +from . import common_utils +from .kaldi_compatibility_impl import Kaldi + + +common_utils.define_test_suites(globals(), [Kaldi], devices=['cpu']) diff --git a/test/kaldi_compatibility_cuda_test.py b/test/kaldi_compatibility_cuda_test.py new file mode 100644 index 0000000000..acaa2408f5 --- /dev/null +++ b/test/kaldi_compatibility_cuda_test.py @@ -0,0 +1,5 @@ +from . import common_utils +from .kaldi_compatibility_impl import Kaldi + + +common_utils.define_test_suites(globals(), [Kaldi], devices=['cuda']) diff --git a/test/kaldi_compatibility_impl.py b/test/kaldi_compatibility_impl.py new file mode 100644 index 0000000000..8fbdff75cb --- /dev/null +++ b/test/kaldi_compatibility_impl.py @@ -0,0 +1,104 @@ +"""Test suites for checking numerical compatibility against Kaldi""" +import shutil +import unittest +import subprocess + +import kaldi_io +import torch +import torchaudio.functional as F +import torchaudio.compliance.kaldi + +from . import common_utils + + +def _not_available(cmd): + return shutil.which(cmd) is None + + +def _convert_args(**kwargs): + args = [] + for key, value in kwargs.items(): + key = '--' + key.replace('_', '-') + value = str(value).lower() if value in [True, False] else str(value) + args.append('%s=%s' % (key, value)) + return args + + +def _run_kaldi(command, input_type, input_value): + """Run provided Kaldi command, pass a tensor and get the resulting tensor + + Arguments: + input_type: str + 'ark' or 'scp' + input_value: + Tensor for 'ark' + string for 'scp' (path to an audio file) + """ + key = 'foo' + process = subprocess.Popen(command, stdin=subprocess.PIPE, stdout=subprocess.PIPE) + if input_type == 'ark': + kaldi_io.write_mat(process.stdin, input_value.cpu().numpy(), key=key) + elif input_type == 'scp': + process.stdin.write(f'{key} {input_value}'.encode('utf8')) + else: + raise NotImplementedError('Unexpected type') + process.stdin.close() + result = dict(kaldi_io.read_mat_ark(process.stdout))['foo'] + return torch.from_numpy(result.copy()) # copy supresses some torch warning + + +class Kaldi(common_utils.TestBaseMixin): + def assert_equal(self, output, *, expected, rtol=None, atol=None): + expected = expected.to(dtype=self.dtype, device=self.device) + self.assertEqual(output, expected, rtol=rtol, atol=atol) + + @unittest.skipIf(_not_available('apply-cmvn-sliding'), '`apply-cmvn-sliding` not available') + def test_sliding_window_cmn(self): + """sliding_window_cmn should be numerically compatible with apply-cmvn-sliding""" + kwargs = { + 'cmn_window': 600, + 'min_cmn_window': 100, + 'center': False, + 'norm_vars': False, + } + + tensor = torch.randn(40, 10, dtype=self.dtype, device=self.device) + result = F.sliding_window_cmn(tensor, **kwargs) + command = ['apply-cmvn-sliding'] + _convert_args(**kwargs) + ['ark:-', 'ark:-'] + kaldi_result = _run_kaldi(command, 'ark', tensor) + self.assert_equal(result, expected=kaldi_result) + + @unittest.skipIf(_not_available('compute-fbank-feats'), '`compute-fbank-feats` not available') + def test_fbank(self): + """fbank should be numerically compatible with compute-fbank-feats""" + kwargs = { + 'blackman_coeff': 4.3926, + 'dither': 0.0, + 'energy_floor': 2.0617, + 'frame_length': 0.5625, + 'frame_shift': 0.0625, + 'high_freq': 4253, + 'htk_compat': True, + 'low_freq': 1367, + 'num_mel_bins': 5, + 'preemphasis_coefficient': 0.84, + 'raw_energy': False, + 'remove_dc_offset': True, + 'round_to_power_of_two': True, + 'snip_edges': True, + 'subtract_mean': False, + 'use_energy': True, + 'use_log_fbank': True, + 'use_power': False, + 'vtln_high': 2112, + 'vtln_low': 1445, + 'vtln_warp': 1.0000, + 'window_type': 'hamming', + + } + wave_file = common_utils.get_asset_path('kaldi_file.wav') + waveform = torchaudio.load_wav(wave_file)[0].to(dtype=self.dtype, device=self.device) + result = torchaudio.compliance.kaldi.fbank(waveform, **kwargs) + command = ['compute-fbank-feats'] + _convert_args(**kwargs) + ['scp:-', 'ark:-'] + kaldi_result = _run_kaldi(command, 'scp', wave_file) + self.assert_equal(result, expected=kaldi_result, rtol=1e-4, atol=1e-8) diff --git a/test/test_batch_consistency.py b/test/test_batch_consistency.py index 675a21e029..c10652d9ca 100644 --- a/test/test_batch_consistency.py +++ b/test/test_batch_consistency.py @@ -2,33 +2,36 @@ import unittest import torch +from torch.testing._internal.common_utils import TestCase import torchaudio import torchaudio.functional as F -import common_utils +from . import common_utils -def _test_batch_consistency(functional, tensor, *args, batch_size=1, atol=1e-8, rtol=1e-5, **kwargs): - # run then batch the result - torch.random.manual_seed(42) - expected = functional(tensor.clone(), *args, **kwargs) - expected = expected.repeat([batch_size] + [1] * expected.dim()) - - # batch the input and run - torch.random.manual_seed(42) - pattern = [batch_size] + [1] * tensor.dim() - computed = functional(tensor.repeat(pattern), *args, **kwargs) - - torch.testing.assert_allclose(computed, expected, rtol=rtol, atol=atol) - - -def _test_batch(functional, tensor, *args, atol=1e-8, rtol=1e-5, **kwargs): - _test_batch_consistency(functional, tensor, *args, batch_size=1, atol=atol, rtol=rtol, **kwargs) - _test_batch_consistency(functional, tensor, *args, batch_size=3, atol=atol, rtol=rtol, **kwargs) - - -class TestFunctional(unittest.TestCase): +class TestFunctional(TestCase): """Test functions defined in `functional` module""" + def assert_batch_consistency( + self, functional, tensor, *args, batch_size=1, atol=1e-8, rtol=1e-5, seed=42, **kwargs): + # run then batch the result + torch.random.manual_seed(seed) + expected = functional(tensor.clone(), *args, **kwargs) + expected = expected.repeat([batch_size] + [1] * expected.dim()) + + # batch the input and run + torch.random.manual_seed(seed) + pattern = [batch_size] + [1] * tensor.dim() + computed = functional(tensor.repeat(pattern), *args, **kwargs) + + self.assertEqual(computed, expected, rtol=rtol, atol=atol) + + def assert_batch_consistencies( + self, functional, tensor, *args, atol=1e-8, rtol=1e-5, seed=42, **kwargs): + self.assert_batch_consistency( + functional, tensor, *args, batch_size=1, atol=atol, rtol=rtol, seed=seed, **kwargs) + self.assert_batch_consistency( + functional, tensor, *args, batch_size=3, atol=atol, rtol=rtol, seed=seed, **kwargs) + def test_griffinlim(self): n_fft = 400 ws = 400 @@ -40,7 +43,7 @@ def test_griffinlim(self): n_iter = 32 length = 1000 tensor = torch.rand((1, 201, 6)) - _test_batch( + self.assert_batch_consistencies( F.griffinlim, tensor, window, n_fft, hop, ws, power, normalize, n_iter, momentum, length, 0, atol=5e-5 ) @@ -54,7 +57,7 @@ def test_detect_pitch_frequency(self): for filename in filenames: filepath = common_utils.get_asset_path(filename) waveform, sample_rate = torchaudio.load(filepath) - _test_batch(F.detect_pitch_frequency, waveform, sample_rate) + self.assert_batch_consistencies(F.detect_pitch_frequency, waveform, sample_rate) def test_istft(self): stft = torch.tensor([ @@ -62,34 +65,39 @@ def test_istft(self): [[0., 0.], [0., 0.], [0., 0.], [0., 0.], [0., 0.]], [[0., 0.], [0., 0.], [0., 0.], [0., 0.], [0., 0.]] ]) - _test_batch(F.istft, stft, n_fft=4, length=4) + self.assert_batch_consistencies(F.istft, stft, n_fft=4, length=4) def test_contrast(self): waveform = torch.rand(2, 100) - 0.5 - _test_batch(F.contrast, waveform, enhancement_amount=80.) + self.assert_batch_consistencies(F.contrast, waveform, enhancement_amount=80.) def test_dcshift(self): waveform = torch.rand(2, 100) - 0.5 - _test_batch(F.dcshift, waveform, shift=0.5, limiter_gain=0.05) + self.assert_batch_consistencies(F.dcshift, waveform, shift=0.5, limiter_gain=0.05) def test_overdrive(self): waveform = torch.rand(2, 100) - 0.5 - _test_batch(F.overdrive, waveform, gain=45, colour=30) + self.assert_batch_consistencies(F.overdrive, waveform, gain=45, colour=30) + + def test_phaser(self): + filepath = common_utils.get_asset_path("whitenoise.wav") + waveform, sample_rate = torchaudio.load(filepath) + self.assert_batch_consistencies(F.phaser, waveform, sample_rate) def test_sliding_window_cmn(self): waveform = torch.randn(2, 1024) - 0.5 - _test_batch(F.sliding_window_cmn, waveform, center=True, norm_vars=True) - _test_batch(F.sliding_window_cmn, waveform, center=True, norm_vars=False) - _test_batch(F.sliding_window_cmn, waveform, center=False, norm_vars=True) - _test_batch(F.sliding_window_cmn, waveform, center=False, norm_vars=False) + self.assert_batch_consistencies(F.sliding_window_cmn, waveform, center=True, norm_vars=True) + self.assert_batch_consistencies(F.sliding_window_cmn, waveform, center=True, norm_vars=False) + self.assert_batch_consistencies(F.sliding_window_cmn, waveform, center=False, norm_vars=True) + self.assert_batch_consistencies(F.sliding_window_cmn, waveform, center=False, norm_vars=False) def test_vad(self): - filepath = common_utils.get_asset_path("vad-hello-mono-32000.wav") + filepath = common_utils.get_asset_path("vad-go-mono-32000.wav") waveform, sample_rate = torchaudio.load(filepath) - _test_batch(F.vad, waveform, sample_rate=sample_rate) + self.assert_batch_consistencies(F.vad, waveform, sample_rate=sample_rate) -class TestTransforms(unittest.TestCase): +class TestTransforms(TestCase): """Test suite for classes defined in `transforms` module""" def test_batch_AmplitudeToDB(self): spec = torch.rand((6, 201)) @@ -100,7 +108,7 @@ def test_batch_AmplitudeToDB(self): # Batch then transform computed = torchaudio.transforms.AmplitudeToDB()(spec.repeat(3, 1, 1)) - torch.testing.assert_allclose(computed, expected) + self.assertEqual(computed, expected) def test_batch_Resample(self): waveform = torch.randn(2, 2786) @@ -111,7 +119,7 @@ def test_batch_Resample(self): # Batch then transform computed = torchaudio.transforms.Resample()(waveform.repeat(3, 1, 1)) - torch.testing.assert_allclose(computed, expected) + self.assertEqual(computed, expected) def test_batch_MelScale(self): specgram = torch.randn(2, 31, 2786) @@ -123,7 +131,7 @@ def test_batch_MelScale(self): computed = torchaudio.transforms.MelScale()(specgram.repeat(3, 1, 1, 1)) # shape = (3, 2, 201, 1394) - torch.testing.assert_allclose(computed, expected) + self.assertEqual(computed, expected) def test_batch_InverseMelScale(self): n_mels = 32 @@ -140,7 +148,7 @@ def test_batch_InverseMelScale(self): # Because InverseMelScale runs SGD on randomly initialized values so they do not yield # exactly same result. For this reason, tolerance is very relaxed here. - torch.testing.assert_allclose(computed, expected, atol=1.0, rtol=1e-5) + self.assertEqual(computed, expected, atol=1.0, rtol=1e-5) def test_batch_compute_deltas(self): specgram = torch.randn(2, 31, 2786) @@ -152,7 +160,7 @@ def test_batch_compute_deltas(self): computed = torchaudio.transforms.ComputeDeltas()(specgram.repeat(3, 1, 1, 1)) # shape = (3, 2, 201, 1394) - torch.testing.assert_allclose(computed, expected) + self.assertEqual(computed, expected) def test_batch_mulaw(self): test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') @@ -167,7 +175,7 @@ def test_batch_mulaw(self): computed = torchaudio.transforms.MuLawEncoding()(waveform_batched) # shape = (3, 2, 201, 1394) - torch.testing.assert_allclose(computed, expected) + self.assertEqual(computed, expected) # Single then transform then batch waveform_decoded = torchaudio.transforms.MuLawDecoding()(waveform_encoded) @@ -177,7 +185,7 @@ def test_batch_mulaw(self): computed = torchaudio.transforms.MuLawDecoding()(computed) # shape = (3, 2, 201, 1394) - torch.testing.assert_allclose(computed, expected) + self.assertEqual(computed, expected) def test_batch_spectrogram(self): test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') @@ -188,7 +196,7 @@ def test_batch_spectrogram(self): # Batch then transform computed = torchaudio.transforms.Spectrogram()(waveform.repeat(3, 1, 1)) - torch.testing.assert_allclose(computed, expected) + self.assertEqual(computed, expected) def test_batch_melspectrogram(self): test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') @@ -199,7 +207,7 @@ def test_batch_melspectrogram(self): # Batch then transform computed = torchaudio.transforms.MelSpectrogram()(waveform.repeat(3, 1, 1)) - torch.testing.assert_allclose(computed, expected) + self.assertEqual(computed, expected) def test_batch_mfcc(self): test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') @@ -210,7 +218,7 @@ def test_batch_mfcc(self): # Batch then transform computed = torchaudio.transforms.MFCC()(waveform.repeat(3, 1, 1)) - torch.testing.assert_allclose(computed, expected, atol=1e-5, rtol=1e-5) + self.assertEqual(computed, expected, atol=1e-4, rtol=1e-5) def test_batch_TimeStretch(self): test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') @@ -244,7 +252,7 @@ def test_batch_TimeStretch(self): hop_length=512, )(complex_specgrams.repeat(3, 1, 1, 1, 1)) - torch.testing.assert_allclose(computed, expected, atol=1e-5, rtol=1e-5) + self.assertEqual(computed, expected, atol=1e-5, rtol=1e-5) def test_batch_Fade(self): test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') @@ -257,7 +265,7 @@ def test_batch_Fade(self): # Batch then transform computed = torchaudio.transforms.Fade(fade_in_len, fade_out_len)(waveform.repeat(3, 1, 1)) - torch.testing.assert_allclose(computed, expected) + self.assertEqual(computed, expected) def test_batch_Vol(self): test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') @@ -268,7 +276,7 @@ def test_batch_Vol(self): # Batch then transform computed = torchaudio.transforms.Vol(gain=1.1)(waveform.repeat(3, 1, 1)) - torch.testing.assert_allclose(computed, expected) + self.assertEqual(computed, expected) if __name__ == '__main__': diff --git a/test/test_compliance_kaldi.py b/test/test_compliance_kaldi.py index 6951ccf59b..2a255a5a4b 100644 --- a/test/test_compliance_kaldi.py +++ b/test/test_compliance_kaldi.py @@ -1,13 +1,13 @@ import math import os -import compliance.utils import torch import torchaudio import torchaudio.compliance.kaldi as kaldi import unittest -import common_utils -from common_utils import AudioBackendScope, BACKENDS +from . import common_utils +from .compliance import utils as compliance_utils +from .common_utils import AudioBackendScope, BACKENDS def extract_window(window, wave, f, frame_length, frame_shift, snip_edges): @@ -50,7 +50,7 @@ class Test_Kaldi(unittest.TestCase): test_filepath = common_utils.get_asset_path('kaldi_file.wav') test_8000_filepath = common_utils.get_asset_path('kaldi_file_8000.wav') kaldi_output_dir = common_utils.get_asset_path('kaldi') - test_filepaths = {prefix: [] for prefix in compliance.utils.TEST_PREFIX} + test_filepaths = {prefix: [] for prefix in compliance_utils.TEST_PREFIX} # separating test files by their types (e.g 'spec', 'fbank', etc.) for f in os.listdir(kaldi_output_dir): @@ -151,7 +151,7 @@ def _compliance_test_helper(self, sound_filepath, filepath_key, expected_num_fil args = f.split('-') args[-1] = os.path.splitext(args[-1])[0] assert len(args) == expected_num_args, 'invalid test kaldi file name' - args = [compliance.utils.parse(arg) for arg in args] + args = [compliance_utils.parse(arg) for arg in args] output = get_output_fn(sound, args) diff --git a/test/test_dataloader.py b/test/test_dataloader.py index 602491ee21..cfcc25e7c4 100644 --- a/test/test_dataloader.py +++ b/test/test_dataloader.py @@ -3,8 +3,8 @@ import torchaudio from torch.utils.data import Dataset, DataLoader -import common_utils -from common_utils import AudioBackendScope, BACKENDS +from . import common_utils +from .common_utils import AudioBackendScope, BACKENDS class TORCHAUDIODS(Dataset): @@ -28,8 +28,9 @@ def __len__(self): return len(self.data) -@unittest.skipIf("sox" not in BACKENDS, "sox not available") class Test_DataLoader(unittest.TestCase): + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") def test_1(self): expected_size = (2, 1, 16000) ds = TORCHAUDIODS() @@ -39,5 +40,4 @@ def test_1(self): if __name__ == '__main__': - with AudioBackendScope("sox"): - unittest.main() + unittest.main() diff --git a/test/test_datasets.py b/test/test_datasets.py index e42e21e999..96c12884d6 100644 --- a/test/test_datasets.py +++ b/test/test_datasets.py @@ -8,7 +8,7 @@ from torchaudio.datasets.yesno import YESNO from torchaudio.datasets.ljspeech import LJSPEECH -import common_utils +from . import common_utils class TestDatasets(unittest.TestCase): @@ -26,10 +26,12 @@ def test_librispeech(self): data = LIBRISPEECH(self.path, "dev-clean") data[0] + @unittest.skipIf("sox" not in common_utils.BACKENDS, "sox not available") def test_commonvoice(self): data = COMMONVOICE(self.path, url="tatar") data[0] + @unittest.skipIf("sox" not in common_utils.BACKENDS, "sox not available") def test_commonvoice_diskcache(self): data = COMMONVOICE(self.path, url="tatar") data = diskcache_iterator(data) @@ -38,6 +40,7 @@ def test_commonvoice_diskcache(self): # Load data[0] + @unittest.skipIf("sox" not in common_utils.BACKENDS, "sox not available") def test_commonvoice_bg(self): data = COMMONVOICE(self.path, url="tatar") data = bg_iterator(data, 5) diff --git a/test/test_functional.py b/test/test_functional.py index a126bd9581..ac90b7bde5 100644 --- a/test/test_functional.py +++ b/test/test_functional.py @@ -6,13 +6,10 @@ import torchaudio.functional as F import pytest -import common_utils +from . import common_utils -class _LfilterMixin: - device = None - dtype = None - +class Lfilter(common_utils.TestBaseMixin): def test_simple(self): """ Create a very basic signal, @@ -26,23 +23,19 @@ def test_simple(self): a_coeffs = torch.tensor([1, 0, 0, 0], dtype=self.dtype, device=self.device) output_waveform = F.lfilter(waveform, a_coeffs, b_coeffs) - torch.testing.assert_allclose(output_waveform[:, 3:], waveform[:, 0:-3], atol=1e-5, rtol=1e-5) - - -class TestLfilterFloat32CPU(_LfilterMixin, unittest.TestCase): - device = torch.device('cpu') - dtype = torch.float32 - + self.assertEqual(output_waveform[:, 3:], waveform[:, 0:-3], atol=1e-5, rtol=1e-5) -class TestLfilterFloat64CPU(_LfilterMixin, unittest.TestCase): - device = torch.device('cpu') - dtype = torch.float64 + def test_clamp(self): + input_signal = torch.ones(1, 44100 * 1, dtype=self.dtype, device=self.device) + b_coeffs = torch.tensor([1, 0], dtype=self.dtype, device=self.device) + a_coeffs = torch.tensor([1, -0.95], dtype=self.dtype, device=self.device) + output_signal = F.lfilter(input_signal, a_coeffs, b_coeffs, clamp=True) + assert output_signal.max() <= 1 + output_signal = F.lfilter(input_signal, a_coeffs, b_coeffs, clamp=False) + assert output_signal.max() > 1 -@unittest.skipIf(not torch.cuda.is_available(), "CUDA not available") -class TestLfilterFloat32CUDA(_LfilterMixin, unittest.TestCase): - device = torch.device('cuda') - dtype = torch.float32 +common_utils.define_test_suites(globals(), [Lfilter]) class TestComputeDeltas(unittest.TestCase): @@ -179,7 +172,7 @@ def test_istft_of_zeros(self): def test_istft_requires_overlap_windows(self): # the window is size 1 but it hops 20 so there is a gap which throw an error stft = torch.zeros((3, 5, 2)) - self.assertRaises(AssertionError, torchaudio.functional.istft, stft, n_fft=4, + self.assertRaises(RuntimeError, torchaudio.functional.istft, stft, n_fft=4, hop_length=20, win_length=1, window=torch.ones(1)) def test_istft_requires_nola(self): @@ -199,11 +192,11 @@ def test_istft_requires_nola(self): # A window of ones meets NOLA but a window of zeros does not. This should # throw an error. torchaudio.functional.istft(stft, **kwargs_ok) - self.assertRaises(AssertionError, torchaudio.functional.istft, stft, **kwargs_not_ok) + self.assertRaises(RuntimeError, torchaudio.functional.istft, stft, **kwargs_not_ok) def test_istft_requires_non_empty(self): - self.assertRaises(AssertionError, torchaudio.functional.istft, torch.zeros((3, 0, 2)), 2) - self.assertRaises(AssertionError, torchaudio.functional.istft, torch.zeros((0, 3, 2)), 2) + self.assertRaises(RuntimeError, torchaudio.functional.istft, torch.zeros((3, 0, 2)), 2) + self.assertRaises(RuntimeError, torchaudio.functional.istft, torch.zeros((0, 3, 2)), 2) def _test_istft_of_sine(self, amplitude, L, n): # stft of amplitude*sin(2*pi/L*n*x) with the hop length and window size equaling L diff --git a/test/test_io.py b/test/test_io.py index 80968e15ad..0295cb785e 100644 --- a/test/test_io.py +++ b/test/test_io.py @@ -3,7 +3,7 @@ import torchaudio import math import os -from common_utils import AudioBackendScope, BACKENDS, BACKENDS_MP3, create_temp_assets_dir +from .common_utils import AudioBackendScope, BACKENDS, BACKENDS_MP3, create_temp_assets_dir class Test_LoadSave(unittest.TestCase): @@ -171,7 +171,7 @@ def _test_3_load_and_save_is_identity(self): self.assertEqual(sample_rate, sample_rate2) os.unlink(output_path) - @unittest.skipIf(set(["sox", "soundfile"]) not in set(BACKENDS), "sox and soundfile are not available") + @unittest.skipIf(any(be not in BACKENDS for be in ["sox", "soundfile"]), "sox and soundfile are not available") def test_3_load_and_save_is_identity_across_backend(self): with self.subTest(): self._test_3_load_and_save_is_identity_across_backend("sox", "soundfile") diff --git a/test/test_kaldi_io.py b/test/test_kaldi_io.py index bc9903a3ef..942b11de35 100644 --- a/test/test_kaldi_io.py +++ b/test/test_kaldi_io.py @@ -3,7 +3,7 @@ import torch import torchaudio.kaldi_io as kio -import common_utils +from . import common_utils class Test_KaldiIO(unittest.TestCase): diff --git a/test/test_librosa_compatibility.py b/test/test_librosa_compatibility.py index 460f379ce9..665341d945 100644 --- a/test/test_librosa_compatibility.py +++ b/test/test_librosa_compatibility.py @@ -1,8 +1,10 @@ """Test suites for numerical compatibility with librosa""" import os import unittest +from distutils.version import StrictVersion import torch +from torch.testing._internal.common_utils import TestCase import torchaudio import torchaudio.functional as F from torchaudio.common_utils import IMPORT_LIBROSA @@ -14,18 +16,11 @@ import pytest -import common_utils +from . import common_utils -class _LibrosaMixin: - """Automatically skip tests if librosa is not available""" - def setUp(self): - super().setUp() - if not IMPORT_LIBROSA: - raise unittest.SkipTest('Librosa not available') - - -class TestFunctional(_LibrosaMixin, unittest.TestCase): +@unittest.skipIf(not IMPORT_LIBROSA, "Librosa not available") +class TestFunctional(TestCase): """Test suite for functions in `functional` module.""" def test_griffinlim(self): # NOTE: This test is flaky without a fixed random seed @@ -51,25 +46,26 @@ def test_griffinlim(self): momentum=momentum, init=init, length=length) lr_out = torch.from_numpy(lr_out).unsqueeze(0) - torch.testing.assert_allclose(ta_out, lr_out, atol=5e-5, rtol=1e-5) + self.assertEqual(ta_out, lr_out, atol=5e-5, rtol=1e-5) - def _test_create_fb(self, n_mels=40, sample_rate=22050, n_fft=2048, fmin=0.0, fmax=8000.0): + def _test_create_fb(self, n_mels=40, sample_rate=22050, n_fft=2048, fmin=0.0, fmax=8000.0, norm=None): librosa_fb = librosa.filters.mel(sr=sample_rate, n_fft=n_fft, n_mels=n_mels, fmax=fmax, fmin=fmin, htk=True, - norm=None) + norm=norm) fb = F.create_fb_matrix(sample_rate=sample_rate, n_mels=n_mels, f_max=fmax, f_min=fmin, - n_freqs=(n_fft // 2 + 1)) + n_freqs=(n_fft // 2 + 1), + norm=norm) for i_mel_bank in range(n_mels): - torch.testing.assert_allclose(fb[:, i_mel_bank], torch.tensor(librosa_fb[i_mel_bank]), - atol=1e-4, rtol=1e-5) + self.assertEqual( + fb[:, i_mel_bank], torch.tensor(librosa_fb[i_mel_bank]), atol=1e-4, rtol=1e-5) def test_create_fb(self): self._test_create_fb() @@ -79,6 +75,14 @@ def test_create_fb(self): self._test_create_fb(n_mels=56, fmin=800.0, fmax=900.0) self._test_create_fb(n_mels=56, fmin=1900.0, fmax=900.0) self._test_create_fb(n_mels=10, fmin=1900.0, fmax=900.0) + if StrictVersion(librosa.__version__) < StrictVersion("0.7.2"): + return + self._test_create_fb(n_mels=128, sample_rate=44100, norm="slaney") + self._test_create_fb(n_mels=128, fmin=2000.0, fmax=5000.0, norm="slaney") + self._test_create_fb(n_mels=56, fmin=100.0, fmax=9000.0, norm="slaney") + self._test_create_fb(n_mels=56, fmin=800.0, fmax=900.0, norm="slaney") + self._test_create_fb(n_mels=56, fmin=1900.0, fmax=900.0, norm="slaney") + self._test_create_fb(n_mels=10, fmin=1900.0, fmax=900.0, norm="slaney") def test_amplitude_to_DB(self): spec = torch.rand((6, 201)) @@ -94,7 +98,7 @@ def test_amplitude_to_DB(self): lr_out = librosa.core.power_to_db(spec.numpy()) lr_out = torch.from_numpy(lr_out) - torch.testing.assert_allclose(ta_out, lr_out, atol=5e-5, rtol=1e-5) + self.assertEqual(ta_out, lr_out, atol=5e-5, rtol=1e-5) # Amplitude to DB multiplier = 20.0 @@ -103,7 +107,7 @@ def test_amplitude_to_DB(self): lr_out = librosa.core.amplitude_to_db(spec.numpy()) lr_out = torch.from_numpy(lr_out) - torch.testing.assert_allclose(ta_out, lr_out, atol=5e-5, rtol=1e-5) + self.assertEqual(ta_out, lr_out, atol=5e-5, rtol=1e-5) @pytest.mark.parametrize('complex_specgrams', [ @@ -154,73 +158,73 @@ def _load_audio_asset(*asset_paths, **kwargs): return sound, sample_rate -def _test_compatibilities(n_fft, hop_length, power, n_mels, n_mfcc, sample_rate): - sound, sample_rate = _load_audio_asset('sinewave.wav') - sound_librosa = sound.cpu().numpy().squeeze() # (64000) - - # test core spectrogram - spect_transform = torchaudio.transforms.Spectrogram( - n_fft=n_fft, hop_length=hop_length, power=power) - out_librosa, _ = librosa.core.spectrum._spectrogram( - y=sound_librosa, n_fft=n_fft, hop_length=hop_length, power=power) - - out_torch = spect_transform(sound).squeeze().cpu() - torch.testing.assert_allclose(out_torch, torch.from_numpy(out_librosa), atol=1e-5, rtol=1e-5) - - # test mel spectrogram - melspect_transform = torchaudio.transforms.MelSpectrogram( - sample_rate=sample_rate, window_fn=torch.hann_window, - hop_length=hop_length, n_mels=n_mels, n_fft=n_fft) - librosa_mel = librosa.feature.melspectrogram( - y=sound_librosa, sr=sample_rate, n_fft=n_fft, - hop_length=hop_length, n_mels=n_mels, htk=True, norm=None) - librosa_mel_tensor = torch.from_numpy(librosa_mel) - torch_mel = melspect_transform(sound).squeeze().cpu() - torch.testing.assert_allclose( - torch_mel.type(librosa_mel_tensor.dtype), librosa_mel_tensor, atol=5e-3, rtol=1e-5) - - # test s2db - power_to_db_transform = torchaudio.transforms.AmplitudeToDB('power', 80.) - power_to_db_torch = power_to_db_transform(spect_transform(sound)).squeeze().cpu() - power_to_db_librosa = librosa.core.spectrum.power_to_db(out_librosa) - torch.testing.assert_allclose(power_to_db_torch, torch.from_numpy(power_to_db_librosa), atol=5e-3, rtol=1e-5) - - mag_to_db_transform = torchaudio.transforms.AmplitudeToDB('magnitude', 80.) - mag_to_db_torch = mag_to_db_transform(torch.abs(sound)).squeeze().cpu() - mag_to_db_librosa = librosa.core.spectrum.amplitude_to_db(sound_librosa) - torch.testing.assert_allclose(mag_to_db_torch, torch.from_numpy(mag_to_db_librosa), atol=5e-3, rtol=1e-5) - - power_to_db_torch = power_to_db_transform(melspect_transform(sound)).squeeze().cpu() - db_librosa = librosa.core.spectrum.power_to_db(librosa_mel) - db_librosa_tensor = torch.from_numpy(db_librosa) - torch.testing.assert_allclose( - power_to_db_torch.type(db_librosa_tensor.dtype), db_librosa_tensor, atol=5e-3, rtol=1e-5) - - # test MFCC - melkwargs = {'hop_length': hop_length, 'n_fft': n_fft} - mfcc_transform = torchaudio.transforms.MFCC( - sample_rate=sample_rate, n_mfcc=n_mfcc, norm='ortho', melkwargs=melkwargs) - - # librosa.feature.mfcc doesn't pass kwargs properly since some of the - # kwargs for melspectrogram and mfcc are the same. We just follow the - # function body in - # https://librosa.github.io/librosa/_modules/librosa/feature/spectral.html#melspectrogram - # to mirror this function call with correct args: - # - # librosa_mfcc = librosa.feature.mfcc( - # y=sound_librosa, sr=sample_rate, n_mfcc = n_mfcc, - # hop_length=hop_length, n_fft=n_fft, htk=True, norm=None, n_mels=n_mels) - - librosa_mfcc = scipy.fftpack.dct(db_librosa, axis=0, type=2, norm='ortho')[:n_mfcc] - librosa_mfcc_tensor = torch.from_numpy(librosa_mfcc) - torch_mfcc = mfcc_transform(sound).squeeze().cpu() - - torch.testing.assert_allclose( - torch_mfcc.type(librosa_mfcc_tensor.dtype), librosa_mfcc_tensor, atol=5e-3, rtol=1e-5) - - -class TestTransforms(_LibrosaMixin, unittest.TestCase): +@unittest.skipIf(not IMPORT_LIBROSA, "Librosa not available") +class TestTransforms(TestCase): """Test suite for functions in `transforms` module.""" + def assert_compatibilities(self, n_fft, hop_length, power, n_mels, n_mfcc, sample_rate): + sound, sample_rate = _load_audio_asset('sinewave.wav') + sound_librosa = sound.cpu().numpy().squeeze() # (64000) + + # test core spectrogram + spect_transform = torchaudio.transforms.Spectrogram( + n_fft=n_fft, hop_length=hop_length, power=power) + out_librosa, _ = librosa.core.spectrum._spectrogram( + y=sound_librosa, n_fft=n_fft, hop_length=hop_length, power=power) + + out_torch = spect_transform(sound).squeeze().cpu() + self.assertEqual(out_torch, torch.from_numpy(out_librosa), atol=1e-5, rtol=1e-5) + + # test mel spectrogram + melspect_transform = torchaudio.transforms.MelSpectrogram( + sample_rate=sample_rate, window_fn=torch.hann_window, + hop_length=hop_length, n_mels=n_mels, n_fft=n_fft) + librosa_mel = librosa.feature.melspectrogram( + y=sound_librosa, sr=sample_rate, n_fft=n_fft, + hop_length=hop_length, n_mels=n_mels, htk=True, norm=None) + librosa_mel_tensor = torch.from_numpy(librosa_mel) + torch_mel = melspect_transform(sound).squeeze().cpu() + self.assertEqual( + torch_mel.type(librosa_mel_tensor.dtype), librosa_mel_tensor, atol=5e-3, rtol=1e-5) + + # test s2db + power_to_db_transform = torchaudio.transforms.AmplitudeToDB('power', 80.) + power_to_db_torch = power_to_db_transform(spect_transform(sound)).squeeze().cpu() + power_to_db_librosa = librosa.core.spectrum.power_to_db(out_librosa) + self.assertEqual(power_to_db_torch, torch.from_numpy(power_to_db_librosa), atol=5e-3, rtol=1e-5) + + mag_to_db_transform = torchaudio.transforms.AmplitudeToDB('magnitude', 80.) + mag_to_db_torch = mag_to_db_transform(torch.abs(sound)).squeeze().cpu() + mag_to_db_librosa = librosa.core.spectrum.amplitude_to_db(sound_librosa) + self.assertEqual(mag_to_db_torch, torch.from_numpy(mag_to_db_librosa), atol=5e-3, rtol=1e-5) + + power_to_db_torch = power_to_db_transform(melspect_transform(sound)).squeeze().cpu() + db_librosa = librosa.core.spectrum.power_to_db(librosa_mel) + db_librosa_tensor = torch.from_numpy(db_librosa) + self.assertEqual( + power_to_db_torch.type(db_librosa_tensor.dtype), db_librosa_tensor, atol=5e-3, rtol=1e-5) + + # test MFCC + melkwargs = {'hop_length': hop_length, 'n_fft': n_fft} + mfcc_transform = torchaudio.transforms.MFCC( + sample_rate=sample_rate, n_mfcc=n_mfcc, norm='ortho', melkwargs=melkwargs) + + # librosa.feature.mfcc doesn't pass kwargs properly since some of the + # kwargs for melspectrogram and mfcc are the same. We just follow the + # function body in + # https://librosa.github.io/librosa/_modules/librosa/feature/spectral.html#melspectrogram + # to mirror this function call with correct args: + # + # librosa_mfcc = librosa.feature.mfcc( + # y=sound_librosa, sr=sample_rate, n_mfcc = n_mfcc, + # hop_length=hop_length, n_fft=n_fft, htk=True, norm=None, n_mels=n_mels) + + librosa_mfcc = scipy.fftpack.dct(db_librosa, axis=0, type=2, norm='ortho')[:n_mfcc] + librosa_mfcc_tensor = torch.from_numpy(librosa_mfcc) + torch_mfcc = mfcc_transform(sound).squeeze().cpu() + + self.assertEqual( + torch_mfcc.type(librosa_mfcc_tensor.dtype), librosa_mfcc_tensor, atol=5e-3, rtol=1e-5) + def test_basics1(self): kwargs = { 'n_fft': 400, @@ -230,7 +234,7 @@ def test_basics1(self): 'n_mfcc': 40, 'sample_rate': 16000 } - _test_compatibilities(**kwargs) + self.assert_compatibilities(**kwargs) def test_basics2(self): kwargs = { @@ -241,7 +245,7 @@ def test_basics2(self): 'n_mfcc': 20, 'sample_rate': 16000 } - _test_compatibilities(**kwargs) + self.assert_compatibilities(**kwargs) # NOTE: Test passes offline, but fails on TravisCI (and CircleCI), see #372. @unittest.skipIf('CI' in os.environ, 'Test is known to fail on CI') @@ -254,7 +258,7 @@ def test_basics3(self): 'n_mfcc': 50, 'sample_rate': 24000 } - _test_compatibilities(**kwargs) + self.assert_compatibilities(**kwargs) def test_basics4(self): kwargs = { @@ -265,7 +269,7 @@ def test_basics4(self): 'n_mfcc': 40, 'sample_rate': 16000 } - _test_compatibilities(**kwargs) + self.assert_compatibilities(**kwargs) @unittest.skipIf("sox" not in common_utils.BACKENDS, "sox not available") @common_utils.AudioBackendScope("sox") @@ -288,7 +292,7 @@ def test_MelScale(self): S=spec_lr, sr=sample_rate, n_fft=n_fft, hop_length=hop_length, win_length=n_fft, center=True, window='hann', n_mels=n_mels, htk=True, norm=None) # Note: Using relaxed rtol instead of atol - torch.testing.assert_allclose(melspec_ta, torch.from_numpy(melspec_lr[None, ...]), atol=1e-8, rtol=1e-3) + self.assertEqual(melspec_ta, torch.from_numpy(melspec_lr[None, ...]), atol=1e-8, rtol=1e-3) def test_InverseMelScale(self): """InverseMelScale transform is comparable to that of librosa""" @@ -331,7 +335,7 @@ def test_InverseMelScale(self): # https://github.com/pytorch/audio/pull/366 for the discussion of the choice of algorithm # https://github.com/pytorch/audio/pull/448/files#r385747021 for the distribution of P-inf # distance over frequencies. - torch.testing.assert_allclose(spec_ta, spec_lr, atol=threshold, rtol=1e-5) + self.assertEqual(spec_ta, spec_lr, atol=threshold, rtol=1e-5) threshold = 1700.0 # This threshold was choosen empirically, based on the following observations diff --git a/test/test_models.py b/test/test_models.py new file mode 100644 index 0000000000..fe8df9a167 --- /dev/null +++ b/test/test_models.py @@ -0,0 +1,30 @@ +import pytest + +import torch +from torchaudio.models import Wav2Letter + + +class TestWav2Letter: + @pytest.mark.parametrize('batch_size', [2]) + @pytest.mark.parametrize('num_features', [1]) + @pytest.mark.parametrize('num_classes', [40]) + @pytest.mark.parametrize('input_length', [320]) + def test_waveform(self, batch_size, num_features, num_classes, input_length): + model = Wav2Letter() + + x = torch.rand(batch_size, num_features, input_length) + out = model(x) + + assert out.size() == (batch_size, num_classes, 2) + + @pytest.mark.parametrize('batch_size', [2]) + @pytest.mark.parametrize('num_features', [13]) + @pytest.mark.parametrize('num_classes', [40]) + @pytest.mark.parametrize('input_length', [2]) + def test_mfcc(self, batch_size, num_features, num_classes, input_length): + model = Wav2Letter(input_type="mfcc", num_features=13) + + x = torch.rand(batch_size, num_features, input_length) + out = model(x) + + assert out.size() == (batch_size, num_classes, 2) diff --git a/test/test_sox_compatibility.py b/test/test_sox_compatibility.py index 508ec7abfa..dfa57cec47 100644 --- a/test/test_sox_compatibility.py +++ b/test/test_sox_compatibility.py @@ -1,15 +1,16 @@ import unittest import torch +from torch.testing._internal.common_utils import TestCase import torchaudio import torchaudio.functional as F import torchaudio.transforms as T -import common_utils -from common_utils import AudioBackendScope, BACKENDS +from . import common_utils +from .common_utils import AudioBackendScope, BACKENDS -class TestFunctionalFiltering(unittest.TestCase): +class TestFunctionalFiltering(TestCase): @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") def test_gain(self): @@ -24,7 +25,7 @@ def test_gain(self): E.append_effect_to_chain("gain", [3]) sox_gain_waveform = E.sox_build_flow_effects()[0] - torch.testing.assert_allclose(waveform_gain, sox_gain_waveform, atol=1e-04, rtol=1e-5) + self.assertEqual(waveform_gain, sox_gain_waveform, atol=1e-04, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -40,13 +41,13 @@ def test_dither(self): E.append_effect_to_chain("dither", []) sox_dither_waveform = E.sox_build_flow_effects()[0] - torch.testing.assert_allclose(waveform_dithered, sox_dither_waveform, atol=1e-04, rtol=1e-5) + self.assertEqual(waveform_dithered, sox_dither_waveform, atol=1e-04, rtol=1e-5) E.clear_chain() E.append_effect_to_chain("dither", ["-s"]) sox_dither_waveform_ns = E.sox_build_flow_effects()[0] - torch.testing.assert_allclose(waveform_dithered_noiseshaped, sox_dither_waveform_ns, atol=1e-02, rtol=1e-5) + self.assertEqual(waveform_dithered_noiseshaped, sox_dither_waveform_ns, atol=1e-02, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -69,7 +70,7 @@ def test_vctk_transform_pipeline(self): E.append_effect_to_chain("dither", ["-s"]) wf_vctk_sox = E.sox_build_flow_effects()[0] - torch.testing.assert_allclose(wf_vctk, wf_vctk_sox, rtol=1e-03, atol=1e-03) + self.assertEqual(wf_vctk, wf_vctk_sox, rtol=1e-03, atol=1e-03) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -89,7 +90,7 @@ def test_lowpass(self): waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) output_waveform = F.lowpass_biquad(waveform, sample_rate, cutoff_freq) - torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -110,7 +111,7 @@ def test_highpass(self): output_waveform = F.highpass_biquad(waveform, sample_rate, cutoff_freq) # TBD - this fails at the 1e-4 level, debug why - torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-3, rtol=1e-5) + self.assertEqual(output_waveform, sox_output_waveform, atol=1e-3, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -131,7 +132,7 @@ def test_allpass(self): waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) output_waveform = F.allpass_biquad(waveform, sample_rate, central_freq, q) - torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -153,7 +154,7 @@ def test_bandpass_with_csg(self): waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) output_waveform = F.bandpass_biquad(waveform, sample_rate, central_freq, q, const_skirt_gain) - torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -175,7 +176,7 @@ def test_bandpass_without_csg(self): waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) output_waveform = F.bandpass_biquad(waveform, sample_rate, central_freq, q, const_skirt_gain) - torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -196,7 +197,7 @@ def test_bandreject(self): waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) output_waveform = F.bandreject_biquad(waveform, sample_rate, central_freq, q) - torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -218,7 +219,7 @@ def test_band_with_noise(self): waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) output_waveform = F.band_biquad(waveform, sample_rate, central_freq, q, noise) - torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -240,7 +241,7 @@ def test_band_without_noise(self): waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) output_waveform = F.band_biquad(waveform, sample_rate, central_freq, q, noise) - torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -262,7 +263,7 @@ def test_treble(self): waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) output_waveform = F.treble_biquad(waveform, sample_rate, gain, central_freq, q) - torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -280,7 +281,7 @@ def test_deemph(self): waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) output_waveform = F.deemph_biquad(waveform, sample_rate) - torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -298,7 +299,7 @@ def test_riaa(self): waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) output_waveform = F.riaa_biquad(waveform, sample_rate) - torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -316,7 +317,7 @@ def test_contrast(self): waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) output_waveform = F.contrast(waveform, enhancement_amount) - torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -335,7 +336,7 @@ def test_dcshift_with_limiter(self): waveform, _ = torchaudio.load(noise_filepath, normalization=True) output_waveform = F.dcshift(waveform, shift, limiter_gain) - torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -353,7 +354,7 @@ def test_dcshift_without_limiter(self): waveform, _ = torchaudio.load(noise_filepath, normalization=True) output_waveform = F.dcshift(waveform, shift) - torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -372,7 +373,51 @@ def test_overdrive(self): waveform, _ = torchaudio.load(noise_filepath, normalization=True) output_waveform = F.overdrive(waveform, gain, colour) - torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") + def test_phaser_sine(self): + """ + Test phaser effect with sine moduldation, compare to SoX implementation + """ + gain_in = 0.5 + gain_out = 0.8 + delay_ms = 2.0 + decay = 0.4 + speed = 0.5 + noise_filepath = common_utils.get_asset_path('whitenoise.wav') + E = torchaudio.sox_effects.SoxEffectsChain() + E.set_input_file(noise_filepath) + E.append_effect_to_chain("phaser", [gain_in, gain_out, delay_ms, decay, speed, "-s"]) + sox_output_waveform, sr = E.sox_build_flow_effects() + + waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) + output_waveform = F.phaser(waveform, sample_rate, gain_in, gain_out, delay_ms, decay, speed, sinusoidal=True) + + self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") + def test_phaser_triangle(self): + """ + Test phaser effect with triangle modulation, compare to SoX implementation + """ + gain_in = 0.5 + gain_out = 0.8 + delay_ms = 2.0 + decay = 0.4 + speed = 0.5 + noise_filepath = common_utils.get_asset_path('whitenoise.wav') + E = torchaudio.sox_effects.SoxEffectsChain() + E.set_input_file(noise_filepath) + E.append_effect_to_chain("phaser", [gain_in, gain_out, delay_ms, decay, speed, "-t"]) + sox_output_waveform, sr = E.sox_build_flow_effects() + + waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) + output_waveform = F.phaser(waveform, sample_rate, gain_in, gain_out, delay_ms, decay, speed, sinusoidal=False) + + self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -394,7 +439,7 @@ def test_equalizer(self): waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) output_waveform = F.equalizer_biquad(waveform, sample_rate, center_freq, gain, q) - torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") @@ -420,7 +465,7 @@ def test_perf_biquad_filtering(self): waveform, torch.tensor([a0, a1, a2]), torch.tensor([b0, b1, b2]) ) - torch.testing.assert_allclose(waveform_lfilter_out, waveform_sox_out, atol=1e-4, rtol=1e-5) + self.assertEqual(waveform_lfilter_out, waveform_sox_out, atol=1e-4, rtol=1e-5) if __name__ == "__main__": diff --git a/test/test_sox_effects.py b/test/test_sox_effects.py index 487d73a53d..213477537e 100644 --- a/test/test_sox_effects.py +++ b/test/test_sox_effects.py @@ -3,14 +3,15 @@ import torchaudio import math -import common_utils -from common_utils import AudioBackendScope, BACKENDS +from . import common_utils +from .common_utils import AudioBackendScope, BACKENDS -@unittest.skipIf("sox" not in BACKENDS, "sox not available") class Test_SoxEffectsChain(unittest.TestCase): test_filepath = common_utils.get_asset_path("steam-train-whistle-daniel_simon.mp3") + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") def test_single_channel(self): fn_sine = common_utils.get_asset_path("sinewave.wav") E = torchaudio.sox_effects.SoxEffectsChain() @@ -20,6 +21,8 @@ def test_single_channel(self): # check if effects worked # print(x.size()) + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") def test_rate_channels(self): target_rate = 16000 target_channels = 1 @@ -32,6 +35,8 @@ def test_rate_channels(self): self.assertEqual(sr, target_rate) self.assertEqual(x.size(0), target_channels) + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") def test_lowpass_speed(self): speed = .8 si, _ = torchaudio.info(self.test_filepath) @@ -44,6 +49,8 @@ def test_lowpass_speed(self): # check if effects worked self.assertEqual(x.size(1), int((si.length / si.channels) / speed)) + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") def test_ulaw_and_siginfo(self): si_out = torchaudio.sox_signalinfo_t() ei_out = torchaudio.sox_encodinginfo_t() @@ -61,6 +68,8 @@ def test_ulaw_and_siginfo(self): self.assertLess(x.unique().size(0), 2**8 + 1) self.assertEqual(x.numel(), si_in.length) + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") def test_band_chorus(self): si_in, ei_in = torchaudio.info(self.test_filepath) ei_in.encoding = torchaudio.get_sox_encoding_t(1) @@ -75,6 +84,8 @@ def test_band_chorus(self): self.assertEqual(x.size(0), si_in.channels) self.assertGreaterEqual(x.size(1) * x.size(0), si_in.length) + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") def test_synth(self): si_in, ei_in = torchaudio.info(self.test_filepath) len_in_seconds = si_in.length / si_in.channels / si_in.rate @@ -88,6 +99,8 @@ def test_synth(self): self.assertEqual(x.size(0), si_in.channels) self.assertEqual(si_in.length, x.size(0) * x.size(1)) + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") def test_gain(self): E = torchaudio.sox_effects.SoxEffectsChain() E.set_input_file(self.test_filepath) @@ -111,6 +124,8 @@ def test_gain(self): E.clear_chain() self.assertLess(x.abs().max().item(), 1.) + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") def test_tempo_or_speed(self): tempo = .8 si, _ = torchaudio.info(self.test_filepath) @@ -144,6 +159,8 @@ def test_tempo_or_speed(self): # check if effect worked self.assertAlmostEqual(x.size(1), math.ceil((si.length / si.channels) / speed), delta=1) + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") def test_trim(self): x_orig, _ = torchaudio.load(self.test_filepath) offset = "10000s" @@ -157,6 +174,8 @@ def test_trim(self): # check if effect worked self.assertTrue(x.allclose(x_orig[:, offset_int:(offset_int + num_frames_int)], rtol=1e-4, atol=1e-4)) + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") def test_silence_contrast(self): si, _ = torchaudio.info(self.test_filepath) E = torchaudio.sox_effects.SoxEffectsChain() @@ -167,6 +186,8 @@ def test_silence_contrast(self): # check if effect worked self.assertLess(x.numel(), si.length) + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") def test_reverse(self): x_orig, _ = torchaudio.load(self.test_filepath) E = torchaudio.sox_effects.SoxEffectsChain() @@ -177,6 +198,8 @@ def test_reverse(self): rev_idx = torch.LongTensor(range(x_orig.size(1))[::-1]) self.assertTrue(x_orig.allclose(x_rev[:, rev_idx], rtol=1e-5, atol=2e-5)) + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") def test_compand_fade(self): E = torchaudio.sox_effects.SoxEffectsChain() E.set_input_file(self.test_filepath) @@ -186,6 +209,8 @@ def test_compand_fade(self): # check if effect worked # print(x.size()) + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") def test_biquad_delay(self): si, _ = torchaudio.info(self.test_filepath) E = torchaudio.sox_effects.SoxEffectsChain() @@ -197,6 +222,8 @@ def test_biquad_delay(self): # check if effect worked self.assertTrue(x.size(1) == (si.length / si.channels) + 15000) + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") def test_invalid_effect_name(self): E = torchaudio.sox_effects.SoxEffectsChain() E.set_input_file(self.test_filepath) @@ -204,6 +231,8 @@ def test_invalid_effect_name(self): with self.assertRaises(LookupError): E.append_effect_to_chain("special", [""]) + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") def test_unimplemented_effect(self): E = torchaudio.sox_effects.SoxEffectsChain() E.set_input_file(self.test_filepath) @@ -211,6 +240,8 @@ def test_unimplemented_effect(self): with self.assertRaises(NotImplementedError): E.append_effect_to_chain("spectrogram", [""]) + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") def test_invalid_effect_options(self): E = torchaudio.sox_effects.SoxEffectsChain() E.set_input_file(self.test_filepath) @@ -219,6 +250,8 @@ def test_invalid_effect_options(self): with self.assertRaises(RuntimeError): E.sox_build_flow_effects() + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") def test_fade(self): x_orig, _ = torchaudio.load(self.test_filepath) fade_in_len = 44100 @@ -235,6 +268,8 @@ def test_fade(self): # check if effect worked self.assertTrue(x.allclose(fade(x_orig), rtol=1e-4, atol=1e-4)) + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") def test_vol(self): x_orig, _ = torchaudio.load(self.test_filepath) @@ -249,10 +284,12 @@ def test_vol(self): # check if effect worked self.assertTrue(x.allclose(z, rtol=1e-4, atol=1e-4)) + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") def test_vad(self): sample_files = [ - common_utils.get_asset_path("vad-hello-stereo-44100.wav"), - common_utils.get_asset_path("vad-hello-mono-32000.wav") + common_utils.get_asset_path("vad-go-stereo-44100.wav"), + common_utils.get_asset_path("vad-go-mono-32000.wav") ] for sample_file in sample_files: @@ -269,5 +306,4 @@ def test_vad(self): if __name__ == '__main__': - with AudioBackendScope("sox"): - unittest.main() + unittest.main() diff --git a/test/test_transforms.py b/test/test_transforms.py index 9c0313ba7c..02faa68992 100644 --- a/test/test_transforms.py +++ b/test/test_transforms.py @@ -2,14 +2,15 @@ import unittest import torch +from torch.testing._internal.common_utils import TestCase import torchaudio import torchaudio.transforms as transforms import torchaudio.functional as F -import common_utils +from . import common_utils -class Tester(unittest.TestCase): +class Tester(TestCase): # create a sinewave signal for testing sample_rate = 16000 @@ -49,7 +50,7 @@ def test_AmplitudeToDB(self): mag_to_db_torch = mag_to_db_transform(torch.abs(waveform)) power_to_db_torch = power_to_db_transform(torch.pow(waveform, 2)) - torch.testing.assert_allclose(mag_to_db_torch, power_to_db_torch) + self.assertEqual(mag_to_db_torch, power_to_db_torch) def test_melscale_load_save(self): specgram = torch.ones(1, 1000, 100) @@ -63,7 +64,7 @@ def test_melscale_load_save(self): fb_copy = melscale_transform_copy.fb self.assertEqual(fb_copy.size(), (1000, 128)) - torch.testing.assert_allclose(fb, fb_copy) + self.assertEqual(fb, fb_copy) def test_melspectrogram_load_save(self): waveform = self.waveform.float() @@ -79,10 +80,10 @@ def test_melspectrogram_load_save(self): fb = mel_spectrogram_transform.mel_scale.fb fb_copy = mel_spectrogram_transform_copy.mel_scale.fb - torch.testing.assert_allclose(window, window_copy) + self.assertEqual(window, window_copy) # the default for n_fft = 400 and n_mels = 128 self.assertEqual(fb_copy.size(), (201, 128)) - torch.testing.assert_allclose(fb, fb_copy) + self.assertEqual(fb, fb_copy) def test_mel2(self): top_db = 80. @@ -205,7 +206,7 @@ def test_compute_deltas_transform_same_as_functional(self, atol=1e-6, rtol=1e-8) computed_transform = transform(specgram) computed_functional = F.compute_deltas(specgram, win_length=win_length) - torch.testing.assert_allclose(computed_functional, computed_transform, atol=atol, rtol=rtol) + self.assertEqual(computed_functional, computed_transform, atol=atol, rtol=rtol) def test_compute_deltas_twochannel(self): specgram = torch.tensor([1., 2., 3., 4.]).repeat(1, 2, 1) @@ -214,7 +215,7 @@ def test_compute_deltas_twochannel(self): transform = transforms.ComputeDeltas(win_length=3) computed = transform(specgram) assert computed.shape == expected.shape, (computed.shape, expected.shape) - torch.testing.assert_allclose(computed, expected, atol=1e-6, rtol=1e-8) + self.assertEqual(computed, expected, atol=1e-6, rtol=1e-8) if __name__ == '__main__': diff --git a/test/torchscript_consistency_cpu_test.py b/test/torchscript_consistency_cpu_test.py new file mode 100644 index 0000000000..51a3a477f0 --- /dev/null +++ b/test/torchscript_consistency_cpu_test.py @@ -0,0 +1,5 @@ +from .common_utils import define_test_suites +from .torchscript_consistency_impl import Functional, Transforms + + +define_test_suites(globals(), [Functional, Transforms], devices=['cpu']) diff --git a/test/torchscript_consistency_cuda_test.py b/test/torchscript_consistency_cuda_test.py new file mode 100644 index 0000000000..0899ba2066 --- /dev/null +++ b/test/torchscript_consistency_cuda_test.py @@ -0,0 +1,5 @@ +from .common_utils import define_test_suites +from .torchscript_consistency_impl import Functional, Transforms + + +define_test_suites(globals(), [Functional, Transforms], devices=['cuda']) diff --git a/test/test_torchscript_consistency.py b/test/torchscript_consistency_impl.py similarity index 74% rename from test/test_torchscript_consistency.py rename to test/torchscript_consistency_impl.py index 5f0b12c76b..a4d3cc79cf 100644 --- a/test/test_torchscript_consistency.py +++ b/test/torchscript_consistency_impl.py @@ -6,36 +6,21 @@ import torchaudio.functional as F import torchaudio.transforms as T -import common_utils +from . import common_utils -def _assert_functional_consistency(func, tensor, device, shape_only=False): - tensor = tensor.to(device) - ts_func = torch.jit.script(func) - output = func(tensor) - ts_output = ts_func(tensor) - - if shape_only: - assert ts_output.shape == output.shape, (ts_output.shape, output.shape) - else: - torch.testing.assert_allclose(ts_output, output) - - -def _assert_transforms_consistency(transform, tensor, device): - tensor = tensor.to(device) - transform = transform.to(device) - ts_transform = torch.jit.script(transform) - output = transform(tensor) - ts_output = ts_transform(tensor) - torch.testing.assert_allclose(ts_output, output) - - -class _FunctionalTestMixin: +class Functional(common_utils.TestBaseMixin): """Implements test for `functinoal` modul that are performed for different devices""" - device = None - def _assert_consistency(self, func, tensor, shape_only=False): - return _assert_functional_consistency(func, tensor, self.device, shape_only=shape_only) + tensor = tensor.to(device=self.device, dtype=self.dtype) + + ts_func = torch.jit.script(func) + output = func(tensor) + ts_output = ts_func(tensor) + if shape_only: + ts_output = ts_output.shape + output = output.shape + self.assertEqual(ts_output, output) def test_spectrogram(self): def func(tensor): @@ -48,7 +33,7 @@ def func(tensor): normalize = False return F.spectrogram(tensor, pad, window, n_fft, hop, ws, power, normalize) - tensor = torch.rand((1, 1000)) + tensor = common_utils.get_whitenoise() self._assert_consistency(func, tensor) def test_griffinlim(self): @@ -80,8 +65,7 @@ def func(tensor): self._assert_consistency(func, tensor) def test_detect_pitch_frequency(self): - filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.mp3') - waveform, _ = torchaudio.load(filepath) + waveform = common_utils.get_sinusoid(sample_rate=44100) def func(tensor): sample_rate = 44100 @@ -99,7 +83,8 @@ def func(_): f_max = 20.0 n_mels = 10 sample_rate = 16000 - return F.create_fb_matrix(n_stft, f_min, f_max, n_mels, sample_rate) + norm = "slaney" + return F.create_fb_matrix(n_stft, f_min, f_max, n_mels, sample_rate, norm) dummy = torch.zeros(1, 1) self._assert_consistency(func, dummy) @@ -142,8 +127,8 @@ def func(tensor): qc = 256 return F.mu_law_encoding(tensor, qc) - tensor = torch.rand((1, 10)) - self._assert_consistency(func, tensor) + waveform = common_utils.get_whitenoise() + self._assert_consistency(func, waveform) def test_mu_law_decoding(self): def func(tensor): @@ -159,7 +144,7 @@ def func(tensor): return F.complex_norm(tensor, power) tensor = torch.randn(1, 2, 1025, 400, 2) - _assert_functional_consistency(func, tensor, self.device) + self._assert_consistency(func, tensor) def test_mask_along_axis(self): def func(tensor): @@ -193,26 +178,28 @@ def test_dither_TPDF(self): def func(tensor): return F.dither(tensor, 'TPDF') - tensor = torch.rand((2, 1000)) + tensor = common_utils.get_whitenoise(n_channels=2) self._assert_consistency(func, tensor, shape_only=True) def test_dither_RPDF(self): def func(tensor): return F.dither(tensor, 'RPDF') - tensor = torch.rand((2, 1000)) + tensor = common_utils.get_whitenoise(n_channels=2) self._assert_consistency(func, tensor, shape_only=True) def test_dither_GPDF(self): def func(tensor): return F.dither(tensor, 'GPDF') - tensor = torch.rand((2, 1000)) + tensor = common_utils.get_whitenoise(n_channels=2) self._assert_consistency(func, tensor, shape_only=True) def test_lfilter(self): - filepath = common_utils.get_asset_path('whitenoise.wav') - waveform, _ = torchaudio.load(filepath, normalization=True) + if self.dtype == torch.float64: + raise unittest.SkipTest("This test is known to fail for float64") + + waveform = common_utils.get_whitenoise() def func(tensor): # Design an IIR lowpass filter using scipy.signal filter design @@ -252,8 +239,10 @@ def func(tensor): self._assert_consistency(func, waveform) def test_lowpass(self): - filepath = common_utils.get_asset_path('whitenoise.wav') - waveform, _ = torchaudio.load(filepath, normalization=True) + if self.dtype == torch.float64: + raise unittest.SkipTest("This test is known to fail for float64") + + waveform = common_utils.get_whitenoise(sample_rate=44100) def func(tensor): sample_rate = 44100 @@ -263,8 +252,10 @@ def func(tensor): self._assert_consistency(func, waveform) def test_highpass(self): - filepath = common_utils.get_asset_path('whitenoise.wav') - waveform, _ = torchaudio.load(filepath, normalization=True) + if self.dtype == torch.float64: + raise unittest.SkipTest("This test is known to fail for float64") + + waveform = common_utils.get_whitenoise(sample_rate=44100) def func(tensor): sample_rate = 44100 @@ -274,8 +265,10 @@ def func(tensor): self._assert_consistency(func, waveform) def test_allpass(self): - filepath = common_utils.get_asset_path('whitenoise.wav') - waveform, _ = torchaudio.load(filepath, normalization=True) + if self.dtype == torch.float64: + raise unittest.SkipTest("This test is known to fail for float64") + + waveform = common_utils.get_whitenoise(sample_rate=44100) def func(tensor): sample_rate = 44100 @@ -286,8 +279,10 @@ def func(tensor): self._assert_consistency(func, waveform) def test_bandpass_with_csg(self): - filepath = common_utils.get_asset_path("whitenoise.wav") - waveform, _ = torchaudio.load(filepath, normalization=True) + if self.dtype == torch.float64: + raise unittest.SkipTest("This test is known to fail for float64") + + waveform = common_utils.get_whitenoise(sample_rate=44100) def func(tensor): sample_rate = 44100 @@ -298,9 +293,11 @@ def func(tensor): self._assert_consistency(func, waveform) - def test_bandpass_withou_csg(self): - filepath = common_utils.get_asset_path("whitenoise.wav") - waveform, _ = torchaudio.load(filepath, normalization=True) + def test_bandpass_without_csg(self): + if self.dtype == torch.float64: + raise unittest.SkipTest("This test is known to fail for float64") + + waveform = common_utils.get_whitenoise(sample_rate=44100) def func(tensor): sample_rate = 44100 @@ -312,8 +309,10 @@ def func(tensor): self._assert_consistency(func, waveform) def test_bandreject(self): - filepath = common_utils.get_asset_path("whitenoise.wav") - waveform, _ = torchaudio.load(filepath, normalization=True) + if self.dtype == torch.float64: + raise unittest.SkipTest("This test is known to fail for float64") + + waveform = common_utils.get_whitenoise(sample_rate=44100) def func(tensor): sample_rate = 44100 @@ -324,8 +323,10 @@ def func(tensor): self._assert_consistency(func, waveform) def test_band_with_noise(self): - filepath = common_utils.get_asset_path("whitenoise.wav") - waveform, _ = torchaudio.load(filepath, normalization=True) + if self.dtype == torch.float64: + raise unittest.SkipTest("This test is known to fail for float64") + + waveform = common_utils.get_whitenoise(sample_rate=44100) def func(tensor): sample_rate = 44100 @@ -337,8 +338,10 @@ def func(tensor): self._assert_consistency(func, waveform) def test_band_without_noise(self): - filepath = common_utils.get_asset_path("whitenoise.wav") - waveform, _ = torchaudio.load(filepath, normalization=True) + if self.dtype == torch.float64: + raise unittest.SkipTest("This test is known to fail for float64") + + waveform = common_utils.get_whitenoise(sample_rate=44100) def func(tensor): sample_rate = 44100 @@ -350,8 +353,10 @@ def func(tensor): self._assert_consistency(func, waveform) def test_treble(self): - filepath = common_utils.get_asset_path("whitenoise.wav") - waveform, _ = torchaudio.load(filepath, normalization=True) + if self.dtype == torch.float64: + raise unittest.SkipTest("This test is known to fail for float64") + + waveform = common_utils.get_whitenoise(sample_rate=44100) def func(tensor): sample_rate = 44100 @@ -363,8 +368,10 @@ def func(tensor): self._assert_consistency(func, waveform) def test_deemph(self): - filepath = common_utils.get_asset_path("whitenoise.wav") - waveform, _ = torchaudio.load(filepath, normalization=True) + if self.dtype == torch.float64: + raise unittest.SkipTest("This test is known to fail for float64") + + waveform = common_utils.get_whitenoise(sample_rate=44100) def func(tensor): sample_rate = 44100 @@ -373,8 +380,10 @@ def func(tensor): self._assert_consistency(func, waveform) def test_riaa(self): - filepath = common_utils.get_asset_path("whitenoise.wav") - waveform, _ = torchaudio.load(filepath, normalization=True) + if self.dtype == torch.float64: + raise unittest.SkipTest("This test is known to fail for float64") + + waveform = common_utils.get_whitenoise(sample_rate=44100) def func(tensor): sample_rate = 44100 @@ -383,8 +392,10 @@ def func(tensor): self._assert_consistency(func, waveform) def test_equalizer(self): - filepath = common_utils.get_asset_path("whitenoise.wav") - waveform, _ = torchaudio.load(filepath, normalization=True) + if self.dtype == torch.float64: + raise unittest.SkipTest("This test is known to fail for float64") + + waveform = common_utils.get_whitenoise(sample_rate=44100) def func(tensor): sample_rate = 44100 @@ -396,8 +407,10 @@ def func(tensor): self._assert_consistency(func, waveform) def test_perf_biquad_filtering(self): - filepath = common_utils.get_asset_path("whitenoise.wav") - waveform, _ = torchaudio.load(filepath, normalization=True) + if self.dtype == torch.float64: + raise unittest.SkipTest("This test is known to fail for float64") + + waveform = common_utils.get_whitenoise() def func(tensor): a = torch.tensor([0.7, 0.2, 0.6], device=tensor.device, dtype=tensor.dtype) @@ -442,8 +455,7 @@ def func(tensor): self._assert_consistency(func, b) def test_contrast(self): - filepath = common_utils.get_asset_path("whitenoise.wav") - waveform, _ = torchaudio.load(filepath, normalization=True) + waveform = common_utils.get_whitenoise() def func(tensor): enhancement_amount = 80. @@ -452,8 +464,7 @@ def func(tensor): self._assert_consistency(func, waveform) def test_dcshift(self): - filepath = common_utils.get_asset_path("whitenoise.wav") - waveform, _ = torchaudio.load(filepath, normalization=True) + waveform = common_utils.get_whitenoise() def func(tensor): shift = 0.5 @@ -463,8 +474,7 @@ def func(tensor): self._assert_consistency(func, waveform) def test_overdrive(self): - filepath = common_utils.get_asset_path("whitenoise.wav") - waveform, _ = torchaudio.load(filepath, normalization=True) + waveform = common_utils.get_whitenoise() def func(tensor): gain = 30. @@ -473,13 +483,31 @@ def func(tensor): self._assert_consistency(func, waveform) + def test_phaser(self): + waveform = common_utils.get_whitenoise(sample_rate=44100) -class _TransformsTestMixin: - """Implements test for Transforms that are performed for different devices""" - device = None + def func(tensor): + gain_in = 0.5 + gain_out = 0.8 + delay_ms = 2.0 + decay = 0.4 + speed = 0.5 + sample_rate = 44100 + return F.phaser(tensor, sample_rate, gain_in, gain_out, delay_ms, decay, speed, sinusoidal=True) + self._assert_consistency(func, waveform) + + +class Transforms(common_utils.TestBaseMixin): + """Implements test for Transforms that are performed for different devices""" def _assert_consistency(self, transform, tensor): - _assert_transforms_consistency(transform, tensor, self.device) + tensor = tensor.to(device=self.device, dtype=self.dtype) + transform = transform.to(device=self.device, dtype=self.dtype) + + ts_transform = torch.jit.script(transform) + output = transform(tensor) + ts_output = ts_transform(tensor) + self.assertEqual(ts_output, output) def test_Spectrogram(self): tensor = torch.rand((1, 1000)) @@ -506,17 +534,16 @@ def test_MFCC(self): self._assert_consistency(T.MFCC(), tensor) def test_Resample(self): - tensor = torch.rand((2, 1000)) - sample_rate = 100. - sample_rate_2 = 50. - self._assert_consistency(T.Resample(sample_rate, sample_rate_2), tensor) + sr1, sr2 = 16000, 8000 + tensor = common_utils.get_whitenoise(sample_rate=sr1) + self._assert_consistency(T.Resample(float(sr1), float(sr2)), tensor) def test_ComplexNorm(self): tensor = torch.rand((1, 2, 201, 2)) self._assert_consistency(T.ComplexNorm(), tensor) def test_MuLawEncoding(self): - tensor = torch.rand((1, 10)) + tensor = common_utils.get_whitenoise() self._assert_consistency(T.MuLawEncoding(), tensor) def test_MuLawDecoding(self): @@ -534,8 +561,7 @@ def test_TimeStretch(self): ) def test_Fade(self): - test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') - waveform, _ = torchaudio.load(test_filepath) + waveform = common_utils.get_whitenoise() fade_in_len = 3000 fade_out_len = 3000 self._assert_consistency(T.Fade(fade_in_len, fade_out_len), waveform) @@ -549,8 +575,7 @@ def test_TimeMasking(self): self._assert_consistency(T.TimeMasking(time_mask_param=30, iid_masks=False), tensor) def test_Vol(self): - test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') - waveform, _ = torchaudio.load(test_filepath) + waveform = common_utils.get_whitenoise() self._assert_consistency(T.Vol(1.1), waveform) def test_SlidingWindowCmn(self): @@ -558,32 +583,6 @@ def test_SlidingWindowCmn(self): self._assert_consistency(T.SlidingWindowCmn(), tensor) def test_Vad(self): - filepath = common_utils.get_asset_path("vad-hello-mono-32000.wav") + filepath = common_utils.get_asset_path("vad-go-mono-32000.wav") waveform, sample_rate = torchaudio.load(filepath) self._assert_consistency(T.Vad(sample_rate=sample_rate), waveform) - - -class TestFunctionalCPU(_FunctionalTestMixin, unittest.TestCase): - """Test suite for Functional module on CPU""" - device = torch.device('cpu') - - -@unittest.skipIf(not torch.cuda.is_available(), 'CUDA not available') -class TestFunctionalCUDA(_FunctionalTestMixin, unittest.TestCase): - """Test suite for Functional module on GPU""" - device = torch.device('cuda') - - -class TestTransformsCPU(_TransformsTestMixin, unittest.TestCase): - """Test suite for Transforms module on CPU""" - device = torch.device('cpu') - - -@unittest.skipIf(not torch.cuda.is_available(), 'CUDA not available') -class TestTransformsCUDA(_TransformsTestMixin, unittest.TestCase): - """Test suite for Transforms module on GPU""" - device = torch.device('cuda') - - -if __name__ == '__main__': - unittest.main() diff --git a/torchaudio/__init__.py b/torchaudio/__init__.py index e748a34bc2..a08eaf6964 100644 --- a/torchaudio/__init__.py +++ b/torchaudio/__init__.py @@ -193,8 +193,8 @@ def save_encinfo(filepath: str, # save data to file src = src.contiguous() - import _torch_sox - _torch_sox.write_audio_file(filepath, src, signalinfo, encodinginfo, filetype) + from . import _torchaudio + _torchaudio.write_audio_file(filepath, src, signalinfo, encodinginfo, filetype) def info(filepath: str) -> Tuple[SignalInfo, EncodingInfo]: @@ -236,8 +236,8 @@ def sox_signalinfo_t() -> SignalInfo: >>> si.length = 0 """ - import _torch_sox - return _torch_sox.sox_signalinfo_t() + from . import _torchaudio + return _torchaudio.sox_signalinfo_t() @_audio_backend_guard("sox") @@ -271,8 +271,8 @@ def sox_encodinginfo_t() -> EncodingInfo: """ - import _torch_sox - ei = _torch_sox.sox_encodinginfo_t() + from . import _torchaudio + ei = _torchaudio.sox_encodinginfo_t() sdo = get_sox_option_t(2) # sox_default_option ei.reverse_bytes = sdo ei.reverse_nibbles = sdo @@ -292,12 +292,12 @@ def get_sox_encoding_t(i: int = None) -> EncodingInfo: sox_encoding_t: A sox_encoding_t type for output encoding """ - import _torch_sox + from . import _torchaudio if i is None: # one can see all possible values using the .__members__ attribute - return _torch_sox.sox_encoding_t + return _torchaudio.sox_encoding_t else: - return _torch_sox.sox_encoding_t(i) + return _torchaudio.sox_encoding_t(i) @_audio_backend_guard("sox") @@ -312,11 +312,11 @@ def get_sox_option_t(i: int = 2) -> Any: sox_option_t: A sox_option_t type """ - import _torch_sox + from . import _torchaudio if i is None: - return _torch_sox.sox_option_t + return _torchaudio.sox_option_t else: - return _torch_sox.sox_option_t(i) + return _torchaudio.sox_option_t(i) @_audio_backend_guard("sox") @@ -332,14 +332,14 @@ def get_sox_bool(i: int = 0) -> Any: sox_bool: A sox_bool type """ - import _torch_sox + from . import _torchaudio if i is None: - return _torch_sox.sox_bool + return _torchaudio.sox_bool else: - return _torch_sox.sox_bool(i) + return _torchaudio.sox_bool(i) -_SOX_INITIALIZED = False +_SOX_INITIALIZED: Optional[bool] = False # This variable has a micro lifecycle. (False -> True -> None) # False: Not initialized # True: Initialized @@ -370,8 +370,8 @@ def initialize_sox() -> int: if _SOX_INITIALIZED is None: raise RuntimeError('SoX effects chain has been already shut down. Can not initialize again.') if not _SOX_INITIALIZED: - import _torch_sox - code = _torch_sox.initialize_sox() + from . import _torchaudio + code = _torchaudio.initialize_sox() if code == _SOX_SUCCESS_CODE: _SOX_INITIALIZED = True atexit.register(shutdown_sox) @@ -394,8 +394,8 @@ def shutdown_sox() -> int: """ global _SOX_INITIALIZED if _SOX_INITIALIZED: - import _torch_sox - code = _torch_sox.shutdown_sox() + from . import _torchaudio + code = _torchaudio.shutdown_sox() if code == _SOX_INITIALIZED: _SOX_INITIALIZED = None return code diff --git a/torchaudio/_sox_backend.py b/torchaudio/_sox_backend.py index 802bd0fe17..3478d30d50 100644 --- a/torchaudio/_sox_backend.py +++ b/torchaudio/_sox_backend.py @@ -35,8 +35,8 @@ def load(filepath: str, if offset < 0: raise ValueError("Expected positive offset value") - import _torch_sox - sample_rate = _torch_sox.read_audio_file( + from . import _torchaudio + sample_rate = _torchaudio.read_audio_file( filepath, out, channels_first, @@ -68,5 +68,5 @@ def save(filepath: str, src: Tensor, sample_rate: int, precision: int = 16, chan def info(filepath: str) -> Tuple[SignalInfo, EncodingInfo]: r"""See torchaudio.info""" - import _torch_sox - return _torch_sox.get_info(filepath) + from . import _torchaudio + return _torchaudio.get_info(filepath) diff --git a/torchaudio/compliance/kaldi.py b/torchaudio/compliance/kaldi.py index e9189b2172..8115c3a25f 100644 --- a/torchaudio/compliance/kaldi.py +++ b/torchaudio/compliance/kaldi.py @@ -33,6 +33,10 @@ WINDOWS = [HAMMING, HANNING, POVEY, RECTANGULAR, BLACKMAN] +def _get_epsilon(device, dtype): + return EPSILON.to(device=device, dtype=dtype) + + def _next_power_of_2(x: int) -> int: r"""Returns the smallest power of 2 that is greater than x """ @@ -60,7 +64,7 @@ def _get_strided(waveform: Tensor, window_size: int, window_shift: int, snip_edg if snip_edges: if num_samples < window_size: - return torch.empty((0, 0)) + return torch.empty((0, 0), dtype=waveform.dtype, device=waveform.device) else: m = 1 + (num_samples - window_size) // window_shift else: @@ -83,24 +87,27 @@ def _get_strided(waveform: Tensor, window_size: int, window_shift: int, snip_edg def _feature_window_function(window_type: str, window_size: int, - blackman_coeff: float) -> Tensor: + blackman_coeff: float, + device: torch.device, + dtype: int, + ) -> Tensor: r"""Returns a window function with the given type and size """ if window_type == HANNING: - return torch.hann_window(window_size, periodic=False) + return torch.hann_window(window_size, periodic=False, device=device, dtype=dtype) elif window_type == HAMMING: - return torch.hamming_window(window_size, periodic=False, alpha=0.54, beta=0.46) + return torch.hamming_window(window_size, periodic=False, alpha=0.54, beta=0.46, device=device, dtype=dtype) elif window_type == POVEY: # like hanning but goes to zero at edges - return torch.hann_window(window_size, periodic=False).pow(0.85) + return torch.hann_window(window_size, periodic=False, device=device, dtype=dtype).pow(0.85) elif window_type == RECTANGULAR: - return torch.ones(window_size) + return torch.ones(window_size, device=device, dtype=dtype) elif window_type == BLACKMAN: a = 2 * math.pi / (window_size - 1) - window_function = torch.arange(window_size) + window_function = torch.arange(window_size, device=device, dtype=dtype) # can't use torch.blackman_window as they use different coefficients return (blackman_coeff - 0.5 * torch.cos(a * window_function) + - (0.5 - blackman_coeff) * torch.cos(2 * a * window_function)) + (0.5 - blackman_coeff) * torch.cos(2 * a * window_function)).to(device=device, dtype=dtype) else: raise Exception('Invalid window type ' + window_type) @@ -110,12 +117,12 @@ def _get_log_energy(strided_input: Tensor, energy_floor: float) -> Tensor: r"""Returns the log energy of size (m) for a strided_input (m,*) """ + device, dtype = strided_input.device, strided_input.dtype log_energy = torch.max(strided_input.pow(2).sum(1), epsilon).log() # size (m) if energy_floor == 0.0: return log_energy - else: - return torch.max(log_energy, - torch.tensor(math.log(energy_floor))) + return torch.max( + log_energy, torch.tensor(math.log(energy_floor), device=device, dtype=dtype)) def _get_waveform_and_window_properties(waveform: Tensor, @@ -160,12 +167,15 @@ def _get_window(waveform: Tensor, Returns: (Tensor, Tensor): strided_input of size (m, ``padded_window_size``) and signal_log_energy of size (m) """ + device, dtype = waveform.device, waveform.dtype + epsilon = _get_epsilon(device, dtype) + # size (m, window_size) strided_input = _get_strided(waveform, window_size, window_shift, snip_edges) if dither != 0.0: # Returns a random number strictly between 0 and 1 - x = torch.max(EPSILON, torch.rand(strided_input.shape)) + x = torch.max(epsilon, torch.rand(strided_input.shape, device=device, dtype=dtype)) rand_gauss = torch.sqrt(-2 * x.log()) * torch.cos(2 * math.pi * x) strided_input = strided_input + rand_gauss * dither @@ -177,7 +187,7 @@ def _get_window(waveform: Tensor, if raw_energy: # Compute the log energy of each row/frame before applying preemphasis and # window function - signal_log_energy = _get_log_energy(strided_input, EPSILON, energy_floor) # size (m) + signal_log_energy = _get_log_energy(strided_input, epsilon, energy_floor) # size (m) if preemphasis_coefficient != 0.0: # strided_input[i,j] -= preemphasis_coefficient * strided_input[i, max(0, j-1)] for all i,j @@ -187,7 +197,7 @@ def _get_window(waveform: Tensor, # Apply window_function to each row/frame window_function = _feature_window_function( - window_type, window_size, blackman_coeff).unsqueeze(0) # size (1, window_size) + window_type, window_size, blackman_coeff, device, dtype).unsqueeze(0) # size (1, window_size) strided_input = strided_input * window_function # size (m, window_size) # Pad columns with zero until we reach size (m, padded_window_size) @@ -198,7 +208,7 @@ def _get_window(waveform: Tensor, # Compute energy after window function (not the raw one) if not raw_energy: - signal_log_energy = _get_log_energy(strided_input, EPSILON, energy_floor) # size (m) + signal_log_energy = _get_log_energy(strided_input, epsilon, energy_floor) # size (m) return strided_input, signal_log_energy @@ -541,12 +551,14 @@ def fbank(waveform: Tensor, Tensor: A fbank identical to what Kaldi would output. The shape is (m, ``num_mel_bins + use_energy``) where m is calculated in _get_strided """ + device, dtype = waveform.device, waveform.dtype + waveform, window_shift, window_size, padded_window_size = _get_waveform_and_window_properties( waveform, channel, sample_frequency, frame_shift, frame_length, round_to_power_of_two, preemphasis_coefficient) if len(waveform) < min_duration * sample_frequency: # signal is too short - return torch.empty(0) + return torch.empty(0, device=device, dtype=dtype) # strided_input, size (m, padded_window_size) and signal_log_energy, size (m) strided_input, signal_log_energy = _get_window( @@ -563,6 +575,7 @@ def fbank(waveform: Tensor, # size (num_mel_bins, padded_window_size // 2) mel_energies, _ = get_mel_banks(num_mel_bins, padded_window_size, sample_frequency, low_freq, high_freq, vtln_low, vtln_high, vtln_warp) + mel_energies = mel_energies.to(device=device, dtype=dtype) # pad right column with zeros and add dimension, size (1, num_mel_bins, padded_window_size // 2 + 1) mel_energies = torch.nn.functional.pad(mel_energies, (0, 1), mode='constant', value=0).unsqueeze(0) @@ -571,7 +584,7 @@ def fbank(waveform: Tensor, mel_energies = (power_spectrum * mel_energies).sum(dim=2) if use_log_fbank: # avoid log of zero (which should be prevented anyway by dithering) - mel_energies = torch.max(mel_energies, EPSILON).log() + mel_energies = torch.max(mel_energies, _get_epsilon(device, dtype)).log() # if use_energy then add it as the last column for htk_compat == true else first column if use_energy: @@ -737,7 +750,9 @@ def _get_LR_indices_and_weights(orig_freq: float, output_samples_in_unit: int, window_width: float, lowpass_cutoff: float, - lowpass_filter_width: int) -> Tuple[Tensor, Tensor]: + lowpass_filter_width: int, + device: torch.device, + dtype: int) -> Tuple[Tensor, Tensor]: r"""Based on LinearResample::SetIndexesAndWeights where it retrieves the weights for resampling as well as the indices in which they are valid. LinearResample (LR) means that the output signal is at linearly spaced intervals (i.e the output signal has a @@ -785,7 +800,7 @@ def _get_LR_indices_and_weights(orig_freq: float, which correspond with min_input_index, size (``output_samples_in_unit``, ``max_weight_width``)). """ assert lowpass_cutoff < min(orig_freq, new_freq) / 2 - output_t = torch.arange(0., output_samples_in_unit) / new_freq + output_t = torch.arange(0., output_samples_in_unit, device=device, dtype=dtype) / new_freq min_t = output_t - window_width max_t = output_t + window_width @@ -795,7 +810,7 @@ def _get_LR_indices_and_weights(orig_freq: float, max_weight_width = num_indices.max() # create a group of weights of size (output_samples_in_unit, max_weight_width) - j = torch.arange(max_weight_width).unsqueeze(0) + j = torch.arange(max_weight_width, device=device, dtype=dtype).unsqueeze(0) input_index = min_input_index.unsqueeze(1) + j delta_t = (input_index / orig_freq) - output_t.unsqueeze(1) @@ -890,6 +905,8 @@ def resample_waveform(waveform: Tensor, Returns: Tensor: The waveform at the new frequency """ + device, dtype = waveform.device, waveform.dtype + assert waveform.dim() == 2 assert orig_freq > 0.0 and new_freq > 0.0 @@ -903,9 +920,9 @@ def resample_waveform(waveform: Tensor, output_samples_in_unit = int(new_freq) // base_freq window_width = lowpass_filter_width / (2.0 * lowpass_cutoff) - first_indices, weights = _get_LR_indices_and_weights(orig_freq, new_freq, output_samples_in_unit, - window_width, lowpass_cutoff, lowpass_filter_width) - weights = weights.to(waveform.device) # TODO Create weights on device directly + first_indices, weights = _get_LR_indices_and_weights( + orig_freq, new_freq, output_samples_in_unit, + window_width, lowpass_cutoff, lowpass_filter_width, device, dtype) assert first_indices.dim() == 1 # TODO figure a better way to do this. conv1d reaches every element i*stride + padding @@ -918,9 +935,9 @@ def resample_waveform(waveform: Tensor, window_size = weights.size(1) tot_output_samp = _get_num_LR_output_samples(wave_len, orig_freq, new_freq) output = torch.zeros((num_channels, tot_output_samp), - device=waveform.device) + device=device, dtype=dtype) # eye size: (num_channels, num_channels, 1) - eye = torch.eye(num_channels, device=waveform.device).unsqueeze(2) + eye = torch.eye(num_channels, device=device, dtype=dtype).unsqueeze(2) for i in range(first_indices.size(0)): wave_to_conv = waveform first_index = int(first_indices[i].item()) diff --git a/torchaudio/torch_sox.cpp b/torchaudio/csrc/sox.cpp similarity index 98% rename from torchaudio/torch_sox.cpp rename to torchaudio/csrc/sox.cpp index 5f5a182ebc..3ae81bef19 100644 --- a/torchaudio/torch_sox.cpp +++ b/torchaudio/csrc/sox.cpp @@ -1,6 +1,4 @@ -#include - -#include +#include #include #include @@ -70,12 +68,6 @@ void read_audio( } } // namespace -struct SoxEffect { - SoxEffect() : ename(""), eopts({""}) { } - std::string ename; - std::vector eopts; -}; - std::tuple get_info( const std::string& file_name ) { @@ -195,7 +187,7 @@ void write_audio_file( } int initialize_sox() { - /* Initializion for sox effects. Only initialize once */ + /* Initialization for sox effects. Only initialize once */ return sox_init(); } @@ -389,7 +381,7 @@ int build_flow_effects(const std::string& file_name, } // namespace audio } // namespace torch -PYBIND11_MODULE(_torch_sox, m) { +PYBIND11_MODULE(_torchaudio, m) { py::class_(m, "SoxEffect") .def(py::init<>()) .def("__repr__", [](const torch::audio::SoxEffect &self) { diff --git a/torchaudio/torch_sox.h b/torchaudio/csrc/sox.h similarity index 88% rename from torchaudio/torch_sox.h rename to torchaudio/csrc/sox.h index 9714fc90ba..8d851c9b21 100644 --- a/torchaudio/torch_sox.h +++ b/torchaudio/csrc/sox.h @@ -1,4 +1,10 @@ +#include + #include +#include +#include + +#include namespace at { struct Tensor; @@ -9,7 +15,7 @@ namespace torch { namespace audio { /// Reads an audio file from the given `path` into the `output` `Tensor` and /// returns the sample rate of the audio file. /// Throws `std::runtime_error` if the audio file could not be opened, or an -/// error ocurred during reading of the audio data. +/// error occurred during reading of the audio data. int read_audio_file( const std::string& file_name, at::Tensor output, @@ -18,24 +24,24 @@ int read_audio_file( int64_t offset, sox_signalinfo_t* si, sox_encodinginfo_t* ei, - const char* ft) + const char* ft); /// Writes the data of a `Tensor` into an audio file at the given `path`, with /// a certain extension (e.g. `wav`or `mp3`) and sample rate. /// Throws `std::runtime_error` when the audio file could not be opened for -/// writing, or an error ocurred during writing of the audio data. +/// writing, or an error occurred during writing of the audio data. void write_audio_file( const std::string& file_name, - at::Tensor& tensor, + const at::Tensor& tensor, sox_signalinfo_t* si, sox_encodinginfo_t* ei, - const char* file_type) + const char* file_type); /// Reads an audio file from the given `path` and returns a tuple of /// sox_signalinfo_t and sox_encodinginfo_t, which contain information about /// the audio file such as sample rate, length, bit precision, encoding and more. /// Throws `std::runtime_error` if the audio file could not be opened, or an -/// error ocurred during reading of the audio data. +/// error occurred during reading of the audio data. std::tuple get_info( const std::string& file_name); diff --git a/torchaudio/functional.py b/torchaudio/functional.py index cb5156596d..f59c46eb14 100644 --- a/torchaudio/functional.py +++ b/torchaudio/functional.py @@ -2,6 +2,7 @@ import math from typing import Optional, Tuple +import warnings import torch from torch import Tensor @@ -34,6 +35,7 @@ "contrast", "dcshift", "overdrive", + "phaser", 'mask_along_axis', 'mask_along_axis_iid', 'sliding_window_cmn', @@ -48,7 +50,7 @@ def istft( win_length: Optional[int] = None, window: Optional[Tensor] = None, center: bool = True, - pad_mode: str = "reflect", + pad_mode: Optional[str] = None, normalized: bool = False, onesided: bool = True, length: Optional[int] = None, @@ -93,8 +95,7 @@ def istft( center (bool, optional): Whether ``input`` was padded on both sides so that the :math:`t`-th frame is centered at time :math:`t \times \text{hop\_length}`. (Default: ``True``) - pad_mode (str, optional): Controls the padding method used when ``center`` is True. (Default: - ``"reflect"``) + pad_mode: This argument was ignored and to be removed. normalized (bool, optional): Whether the STFT was normalized. (Default: ``False``) onesided (bool, optional): Whether the STFT is onesided. (Default: ``True``) length (int or None, optional): The amount to trim the signal by (i.e. the @@ -103,105 +104,16 @@ def istft( Returns: Tensor: Least squares estimation of the original signal of size (..., signal_length) """ - stft_matrix_dim = stft_matrix.dim() - assert 3 <= stft_matrix_dim, "Incorrect stft dimension: %d" % (stft_matrix_dim) - assert stft_matrix.numel() > 0 - - if stft_matrix_dim == 3: - # add a channel dimension - stft_matrix = stft_matrix.unsqueeze(0) - - # pack batch - shape = stft_matrix.size() - stft_matrix = stft_matrix.view(-1, shape[-3], shape[-2], shape[-1]) - - dtype = stft_matrix.dtype - device = stft_matrix.device - fft_size = stft_matrix.size(1) - assert (onesided and n_fft // 2 + 1 == fft_size) or ( - not onesided and n_fft == fft_size - ), ( - "one_sided implies that n_fft // 2 + 1 == fft_size and not one_sided implies n_fft == fft_size. " - + "Given values were onesided: %s, n_fft: %d, fft_size: %d" - % ("True" if onesided else False, n_fft, fft_size) - ) - - # use stft defaults for Optionals - if win_length is None: - win_length = n_fft - - if hop_length is None: - hop_length = int(win_length // 4) - - # There must be overlap - assert 0 < hop_length <= win_length - assert 0 < win_length <= n_fft - - if window is None: - window = torch.ones(win_length, device=device, dtype=dtype) - - assert window.dim() == 1 and window.size(0) == win_length - - if win_length != n_fft: - # center window with pad left and right zeros - left = (n_fft - win_length) // 2 - window = torch.nn.functional.pad(window, (left, n_fft - win_length - left)) - assert window.size(0) == n_fft - # win_length and n_fft are synonymous from here on - - stft_matrix = stft_matrix.transpose(1, 2) # size (channel, n_frame, fft_size, 2) - stft_matrix = torch.irfft( - stft_matrix, 1, normalized, onesided, signal_sizes=(n_fft,) - ) # size (channel, n_frame, n_fft) - - assert stft_matrix.size(2) == n_fft - n_frame = stft_matrix.size(1) - - ytmp = stft_matrix * window.view(1, 1, n_fft) # size (channel, n_frame, n_fft) - # each column of a channel is a frame which needs to be overlap added at the right place - ytmp = ytmp.transpose(1, 2) # size (channel, n_fft, n_frame) - - # this does overlap add where the frames of ytmp are added such that the i'th frame of - # ytmp is added starting at i*hop_length in the output - y = torch.nn.functional.fold( - ytmp, (1, (n_frame - 1) * hop_length + n_fft), (1, n_fft), stride=(1, hop_length) - ).squeeze(2) - - # do the same for the window function - window_sq = ( - window.pow(2).view(n_fft, 1).repeat((1, n_frame)).unsqueeze(0) - ) # size (1, n_fft, n_frame) - window_envelop = torch.nn.functional.fold( - window_sq, (1, (n_frame - 1) * hop_length + n_fft), (1, n_fft), stride=(1, hop_length) - ).squeeze(2) # size (1, 1, expected_signal_len) - - expected_signal_len = n_fft + hop_length * (n_frame - 1) - assert y.size(2) == expected_signal_len - assert window_envelop.size(2) == expected_signal_len - - half_n_fft = n_fft // 2 - # we need to trim the front padding away if center - start = half_n_fft if center else 0 - end = -half_n_fft if length is None else start + length - - y = y[:, :, start:end] - window_envelop = window_envelop[:, :, start:end] - - # check NOLA non-zero overlap condition - window_envelop_lowest = window_envelop.abs().min() - assert window_envelop_lowest > 1e-11, "window overlap add min: %f" % ( - window_envelop_lowest - ) - - y = (y / window_envelop).squeeze(1) # size (channel, expected_signal_len) - - # unpack batch - y = y.view(shape[:-3] + y.shape[-1:]) - - if stft_matrix_dim == 3: # remove the channel dimension - y = y.squeeze(0) - - return y + warnings.warn( + 'istft has been moved to PyTorch and will be removed from torchaudio, ' + 'please use torch.istft instead.') + if pad_mode is not None: + warnings.warn( + 'The parameter `pad_mode` was ignored in isftft, and is thus being deprecated. ' + 'Please set `pad_mode` to None to suppress this warning.') + return torch.istft( + input=stft_matrix, n_fft=n_fft, hop_length=hop_length, win_length=win_length, window=window, + center=center, normalized=normalized, onesided=onesided, length=length) def spectrogram( @@ -241,7 +153,7 @@ def spectrogram( # pack batch shape = waveform.size() - waveform = waveform.view(-1, shape[-1]) + waveform = waveform.reshape(-1, shape[-1]) # default values are consistent with librosa.core.spectrum._spectrogram spec_f = torch.stft( @@ -249,7 +161,7 @@ def spectrogram( ) # unpack batch - spec_f = spec_f.view(shape[:-1] + spec_f.shape[-3:]) + spec_f = spec_f.reshape(shape[:-1] + spec_f.shape[-3:]) if normalized: spec_f /= window.pow(2.).sum().sqrt() @@ -310,11 +222,11 @@ def griffinlim( torch.Tensor: waveform of (..., time), where time equals the ``length`` parameter if given. """ assert momentum < 1, 'momentum=%s > 1 can be unstable' % momentum - assert momentum > 0, 'momentum=%s < 0' % momentum + assert momentum >= 0, 'momentum=%s < 0' % momentum # pack batch shape = specgram.size() - specgram = specgram.view([-1] + list(shape[-2:])) + specgram = specgram.reshape([-1] + list(shape[-2:])) specgram = specgram.pow(1 / power) @@ -348,7 +260,9 @@ def griffinlim( True, 'reflect', False, True) # Update our phase estimates - angles = rebuilt - tprev.mul_(momentum / (1 + momentum)) + angles = rebuilt + if momentum: + angles = angles - tprev.mul_(momentum / (1 + momentum)) angles = angles.div_(complex_norm(angles).add_(1e-16).unsqueeze(-1).expand_as(angles)) # Return the final phase estimates @@ -360,7 +274,7 @@ def griffinlim( length=length) # unpack batch - waveform = waveform.view(shape[:-2] + waveform.shape[-1:]) + waveform = waveform.reshape(shape[:-2] + waveform.shape[-1:]) return waveform @@ -421,7 +335,8 @@ def create_fb_matrix( f_min: float, f_max: float, n_mels: int, - sample_rate: int + sample_rate: int, + norm: Optional[str] = None ) -> Tensor: r"""Create a frequency bin conversion matrix. @@ -431,6 +346,8 @@ def create_fb_matrix( f_max (float): Maximum frequency (Hz) n_mels (int): Number of mel filterbanks sample_rate (int): Sample rate of the audio waveform + norm (Optional[str]): If 'slaney', divide the triangular mel weights by the width of the mel band + (area normalization). (Default: ``None``) Returns: Tensor: Triangular filter banks (fb matrix) of size (``n_freqs``, ``n_mels``) @@ -439,6 +356,10 @@ def create_fb_matrix( size (..., ``n_freqs``), the applied result would be ``A * create_fb_matrix(A.size(-1), ...)``. """ + + if norm is not None and norm != "slaney": + raise ValueError("norm must be one of None or 'slaney'") + # freq bins # Equivalent filterbank construction by Librosa all_freqs = torch.linspace(0, sample_rate // 2, n_freqs) @@ -458,6 +379,12 @@ def create_fb_matrix( down_slopes = (-1.0 * slopes[:, :-2]) / f_diff[:-1] # (n_freqs, n_mels) up_slopes = slopes[:, 2:] / f_diff[1:] # (n_freqs, n_mels) fb = torch.max(zero, torch.min(down_slopes, up_slopes)) + + if norm is not None and norm == "slaney": + # Slaney-style mel is scaled to be approx constant energy per channel + enorm = 2.0 / (f_pts[2:n_mels + 2] - f_pts[:n_mels]) + fb *= enorm.unsqueeze(0) + return fb @@ -623,7 +550,7 @@ def phase_vocoder( # pack batch shape = complex_specgrams.size() - complex_specgrams = complex_specgrams.view([-1] + list(shape[-3:])) + complex_specgrams = complex_specgrams.reshape([-1] + list(shape[-3:])) time_steps = torch.arange(0, complex_specgrams.size(-2), @@ -663,7 +590,7 @@ def phase_vocoder( complex_specgrams_stretch = torch.stack([real_stretch, imag_stretch], dim=-1) # unpack batch - complex_specgrams_stretch = complex_specgrams_stretch.view(shape[:-3] + complex_specgrams_stretch.shape[1:]) + complex_specgrams_stretch = complex_specgrams_stretch.reshape(shape[:-3] + complex_specgrams_stretch.shape[1:]) return complex_specgrams_stretch @@ -671,25 +598,27 @@ def phase_vocoder( def lfilter( waveform: Tensor, a_coeffs: Tensor, - b_coeffs: Tensor + b_coeffs: Tensor, + clamp: bool = True, ) -> Tensor: r"""Perform an IIR filter by evaluating difference equation. Args: - waveform (Tensor): audio waveform of dimension of `(..., time)`. Must be normalized to -1 to 1. - a_coeffs (Tensor): denominator coefficients of difference equation of dimension of `(n_order + 1)`. - Lower delays coefficients are first, e.g. `[a0, a1, a2, ...]`. + waveform (Tensor): audio waveform of dimension of ``(..., time)``. Must be normalized to -1 to 1. + a_coeffs (Tensor): denominator coefficients of difference equation of dimension of ``(n_order + 1)``. + Lower delays coefficients are first, e.g. ``[a0, a1, a2, ...]``. Must be same size as b_coeffs (pad with 0's as necessary). - b_coeffs (Tensor): numerator coefficients of difference equation of dimension of `(n_order + 1)`. - Lower delays coefficients are first, e.g. `[b0, b1, b2, ...]`. + b_coeffs (Tensor): numerator coefficients of difference equation of dimension of ``(n_order + 1)``. + Lower delays coefficients are first, e.g. ``[b0, b1, b2, ...]``. Must be same size as a_coeffs (pad with 0's as necessary). + clamp (bool, optional): If ``True``, clamp the output signal to be in the range [-1, 1] (Default: ``True``) Returns: - Tensor: Waveform with dimension of `(..., time)`. Output will be clipped to -1 to 1. + Tensor: Waveform with dimension of ``(..., time)``. """ # pack batch shape = waveform.size() - waveform = waveform.view(-1, shape[-1]) + waveform = waveform.reshape(-1, shape[-1]) assert (a_coeffs.size(0) == b_coeffs.size(0)) assert (len(waveform.size()) == 2) @@ -729,10 +658,13 @@ def lfilter( o0.addmv_(windowed_output_signal, a_coeffs_flipped, alpha=-1) padded_output_waveform[:, i_sample + n_order - 1] = o0 - output = torch.clamp(padded_output_waveform[:, (n_order - 1):], min=-1., max=1.) + output = padded_output_waveform[:, (n_order - 1):] + + if clamp: + output = torch.clamp(output, min=-1., max=1.) # unpack batch - output = output.view(shape[:-1] + output.shape[-1:]) + output = output.reshape(shape[:-1] + output.shape[-1:]) return output @@ -1296,6 +1228,135 @@ def overdrive( return output_waveform.clamp(min=-1, max=1).view(actual_shape) +def phaser( + waveform: Tensor, + sample_rate: int, + gain_in: float = 0.4, + gain_out: float = 0.74, + delay_ms: float = 3.0, + decay: float = 0.4, + mod_speed: float = 0.5, + sinusoidal: bool = True +) -> Tensor: + r"""Apply a phasing effect to the audio. Similar to SoX implementation. + + Args: + waveform (Tensor): audio waveform of dimension of `(..., time)` + sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz) + gain_in (float): desired input gain at the boost (or attenuation) in dB + Allowed range of values are 0 to 1 + gain_out (float): desired output gain at the boost (or attenuation) in dB + Allowed range of values are 0 to 1e9 + delay_ms (float): desired delay in milli seconds + Allowed range of values are 0 to 5.0 + decay (float): desired decay relative to gain-in + Allowed range of values are 0 to 0.99 + mod_speed (float): modulation speed in Hz + Allowed range of values are 0.1 to 2 + sinusoidal (bool): If ``True``, uses sinusoidal modulation (preferable for multiple instruments) + If ``False``, uses triangular modulation (gives single instruments a sharper phasing effect) + (Default: ``True``) + + Returns: + Tensor: Waveform of dimension of `(..., time)` + + References: + http://sox.sourceforge.net/sox.html + Scott Lehman, Effects Explained, http://harmony-central.com/Effects/effects-explained.html + """ + actual_shape = waveform.shape + device, dtype = waveform.device, waveform.dtype + + # convert to 2D (channels,time) + waveform = waveform.view(-1, actual_shape[-1]) + + delay_buf_len = int((delay_ms * .001 * sample_rate) + .5) + delay_buf = torch.zeros(waveform.shape[0], delay_buf_len, dtype=dtype, device=device) + + mod_buf_len = int(sample_rate / mod_speed + .5) + mod_buf = torch.zeros(mod_buf_len, dtype=dtype, device=device) + + if sinusoidal: + wave_type = 'SINE' + else: + wave_type = 'TRIANGLE' + + mod_buf = _generate_wave_table(wave_type=wave_type, + data_type='INT', + table_size=mod_buf_len, + min=1., + max=float(delay_buf_len), + phase=math.pi / 2) + + delay_pos = 0 + mod_pos = 0 + + output_waveform = torch.zeros_like(waveform, dtype=dtype, device=device) + + for i in range(waveform.shape[-1]): + idx = int((delay_pos + mod_buf[mod_pos]) % delay_buf_len) + temp = (waveform[:, i] * gain_in) + (delay_buf[:, idx] * decay) + mod_pos = (mod_pos + 1) % mod_buf_len + delay_pos = (delay_pos + 1) % delay_buf_len + delay_buf[:, delay_pos] = temp + output_waveform[:, i] = temp * gain_out + + return output_waveform.clamp(min=-1, max=1).view(actual_shape) + + +def _generate_wave_table( + wave_type: str, + data_type: str, + table_size: int, + min: float, + max: float, + phase: float +) -> Tensor: + r"""A helper fucntion for phaser. Generates a table with given parameters + + Args: + wave_type (str): SINE or TRIANGULAR + data_type (str): desired data_type ( `INT` or `FLOAT` ) + table_size (int): desired table size + min (float): desired min value + max (float): desired max value + phase (float): desired phase + + Returns: + Tensor: A 1D tensor with wave table values + """ + + phase_offset = int(phase / math.pi / 2 * table_size + 0.5) + + t = torch.arange(table_size).to(torch.int32) + + point = (t + phase_offset) % table_size + + d = torch.zeros_like(point).to(torch.float64) + + if wave_type == 'SINE': + d = (torch.sin(point.to(torch.float64) / table_size * 2 * math.pi) + 1) / 2 + elif wave_type == 'TRIANGLE': + d = point.to(torch.float64) * 2 / table_size + value = 4 * point / table_size + d[value == 0] = d[value == 0] + 0.5 + d[value == 1] = 1.5 - d[value == 1] + d[value == 2] = 1.5 - d[value == 2] + d[value == 3] = d[value == 3] - 1.5 + + d = d * (max - min) + min + + if data_type == 'INT': + mask = d < 0 + d[mask] = d[mask] - 0.5 + d[~mask] = d[~mask] + 0.5 + d = d.to(torch.int32) + elif data_type == 'FLOAT': + d = d.to(torch.float32) + + return d + + def mask_along_axis_iid( specgrams: Tensor, mask_param: int, @@ -1305,7 +1366,6 @@ def mask_along_axis_iid( r""" Apply a mask along ``axis``. Mask will be applied from indices ``[v_0, v_0 + v)``, where ``v`` is sampled from ``uniform(0, mask_param)``, and ``v_0`` from ``uniform(0, max_v - v)``. - All examples will have the same mask interval. Args: specgrams (Tensor): Real spectrograms (batch, channel, freq, time) @@ -1362,7 +1422,7 @@ def mask_along_axis( # pack batch shape = specgram.size() - specgram = specgram.view([-1] + list(shape[-2:])) + specgram = specgram.reshape([-1] + list(shape[-2:])) value = torch.rand(1) * mask_param min_value = torch.rand(1) * (specgram.size(axis) - value) @@ -1379,7 +1439,7 @@ def mask_along_axis( raise ValueError('Only Frequency and Time masking are supported') # unpack batch - specgram = specgram.view(shape[:-2] + specgram.shape[-2:]) + specgram = specgram.reshape(shape[:-2] + specgram.shape[-2:]) return specgram @@ -1416,7 +1476,7 @@ def compute_deltas( # pack batch shape = specgram.size() - specgram = specgram.view(1, -1, shape[-1]) + specgram = specgram.reshape(1, -1, shape[-1]) assert win_length >= 3 @@ -1432,7 +1492,7 @@ def compute_deltas( output = torch.nn.functional.conv1d(specgram, kernel, groups=specgram.shape[1]) / denom # unpack batch - output = output.view(shape) + output = output.reshape(shape) return output @@ -1466,10 +1526,11 @@ def _add_noise_shaping( error[n] = dithered[n] - original[n] noise_shaped_waveform[n] = dithered[n] + error[n-1] """ - waveform = waveform.view(-1, waveform.size()[-1]) + wf_shape = waveform.size() + waveform = waveform.reshape(-1, wf_shape[-1]) dithered_shape = dithered_waveform.size() - dithered_waveform = dithered_waveform.view(-1, dithered_shape[-1]) + dithered_waveform = dithered_waveform.reshape(-1, dithered_shape[-1]) error = dithered_waveform - waveform @@ -1480,7 +1541,7 @@ def _add_noise_shaping( error[index] = error_offset[:waveform.size()[1]] noise_shaped = dithered_waveform + error - return noise_shaped.view(dithered_shape[:-1] + noise_shaped.shape[-1:]) + return noise_shaped.reshape(dithered_shape[:-1] + noise_shaped.shape[-1:]) def _apply_probability_distribution( @@ -1513,7 +1574,7 @@ def _apply_probability_distribution( # pack batch shape = waveform.size() - waveform = waveform.view(-1, shape[-1]) + waveform = waveform.reshape(-1, shape[-1]) channel_size = waveform.size()[0] - 1 time_size = waveform.size()[-1] - 1 @@ -1554,7 +1615,7 @@ def _apply_probability_distribution( quantised_signal = quantised_signal_scaled / down_scaling # unpack batch - return quantised_signal.view(shape[:-1] + quantised_signal.shape[-1:]) + return quantised_signal.reshape(shape[:-1] + quantised_signal.shape[-1:]) def dither( @@ -1732,7 +1793,7 @@ def detect_pitch_frequency( """ # pack batch shape = list(waveform.size()) - waveform = waveform.view([-1] + shape[-1:]) + waveform = waveform.reshape([-1] + shape[-1:]) nccf = _compute_nccf(waveform, sample_rate, frame_time, freq_low) indices = _find_max_per_frame(nccf, sample_rate, freq_high) @@ -1743,7 +1804,7 @@ def detect_pitch_frequency( freq = sample_rate / (EPSILON + indices.to(torch.float)) # unpack batch - freq = freq.view(shape[:-1] + list(freq.shape[-1:])) + freq = freq.reshape(shape[:-1] + list(freq.shape[-1:])) return freq diff --git a/torchaudio/models/__init__.py b/torchaudio/models/__init__.py new file mode 100644 index 0000000000..1abdac6271 --- /dev/null +++ b/torchaudio/models/__init__.py @@ -0,0 +1 @@ +from .wav2letter import * diff --git a/torchaudio/models/wav2letter.py b/torchaudio/models/wav2letter.py new file mode 100644 index 0000000000..3466e42dd2 --- /dev/null +++ b/torchaudio/models/wav2letter.py @@ -0,0 +1,74 @@ +from typing import Optional + +from torch import Tensor +from torch import nn + +__all__ = ["Wav2Letter"] + + +class Wav2Letter(nn.Module): + r"""Wav2Letter model architecture from the `"Wav2Letter: an End-to-End ConvNet-based Speech Recognition System" + `_ paper. + + :math:`\text{padding} = \frac{\text{ceil}(\text{kernel} - \text{stride})}{2}` + + Args: + num_classes (int, optional): Number of classes to be classified. (Default: ``40``) + input_type (str, optional): Wav2Letter can use as input: ``waveform``, ``power_spectrum`` + or ``mfcc`` (Default: ``waveform``). + num_features (int, optional): Number of input features that the network will receive (Default: ``1``). + """ + + def __init__(self, num_classes: int = 40, + input_type: str = "waveform", + num_features: int = 1) -> None: + super(Wav2Letter, self).__init__() + + acoustic_num_features = 250 if input_type == "waveform" else num_features + acoustic_model = nn.Sequential( + nn.Conv1d(in_channels=acoustic_num_features, out_channels=250, kernel_size=48, stride=2, padding=23), + nn.ReLU(inplace=True), + nn.Conv1d(in_channels=250, out_channels=250, kernel_size=7, stride=1, padding=3), + nn.ReLU(inplace=True), + nn.Conv1d(in_channels=250, out_channels=250, kernel_size=7, stride=1, padding=3), + nn.ReLU(inplace=True), + nn.Conv1d(in_channels=250, out_channels=250, kernel_size=7, stride=1, padding=3), + nn.ReLU(inplace=True), + nn.Conv1d(in_channels=250, out_channels=250, kernel_size=7, stride=1, padding=3), + nn.ReLU(inplace=True), + nn.Conv1d(in_channels=250, out_channels=250, kernel_size=7, stride=1, padding=3), + nn.ReLU(inplace=True), + nn.Conv1d(in_channels=250, out_channels=250, kernel_size=7, stride=1, padding=3), + nn.ReLU(inplace=True), + nn.Conv1d(in_channels=250, out_channels=250, kernel_size=7, stride=1, padding=3), + nn.ReLU(inplace=True), + nn.Conv1d(in_channels=250, out_channels=2000, kernel_size=32, stride=1, padding=16), + nn.ReLU(inplace=True), + nn.Conv1d(in_channels=2000, out_channels=2000, kernel_size=1, stride=1, padding=0), + nn.ReLU(inplace=True), + nn.Conv1d(in_channels=2000, out_channels=num_classes, kernel_size=1, stride=1, padding=0), + nn.ReLU(inplace=True) + ) + + if input_type == "waveform": + waveform_model = nn.Sequential( + nn.Conv1d(in_channels=num_features, out_channels=250, kernel_size=250, stride=160, padding=45), + nn.ReLU(inplace=True) + ) + self.acoustic_model = nn.Sequential(waveform_model, acoustic_model) + + if input_type in ["power_spectrum", "mfcc"]: + self.acoustic_model = acoustic_model + + def forward(self, x: Tensor) -> Tensor: + r""" + Args: + x (Tensor): Tensor of dimension (batch_size, num_features, input_length). + + Returns: + Tensor: Predictor tensor of dimension (batch_size, number_of_classes, input_length). + """ + + x = self.acoustic_model(x) + x = nn.functional.log_softmax(x, dim=1) + return x diff --git a/torchaudio/sox_effects.py b/torchaudio/sox_effects.py index 472d291902..d1eb04e53f 100644 --- a/torchaudio/sox_effects.py +++ b/torchaudio/sox_effects.py @@ -16,8 +16,8 @@ def effect_names() -> List[str]: >>> EFFECT_NAMES = torchaudio.sox_effects.effect_names() """ - import _torch_sox - return _torch_sox.get_effect_names() + from . import _torchaudio + return _torchaudio.get_effect_names() @_audio_backend_guard("sox") @@ -29,8 +29,8 @@ def SoxEffect(): name of effect, and eopts (List[str]) which is a list of effect options. """ - import _torch_sox - return _torch_sox.SoxEffect() + from . import _torchaudio + return _torchaudio.SoxEffect() class SoxEffectsChain(object): @@ -150,15 +150,15 @@ def sox_build_flow_effects(self, # print("effect options:", [x.eopts for x in self.chain]) torchaudio.initialize_sox() - import _torch_sox - sr = _torch_sox.build_flow_effects(self.input_file, - out, - self.channels_first, - self.out_siginfo, - self.out_encinfo, - self.filetype, - self.chain, - self.MAX_EFFECT_OPTS) + from . import _torchaudio + sr = _torchaudio.build_flow_effects(self.input_file, + out, + self.channels_first, + self.out_siginfo, + self.out_encinfo, + self.filetype, + self.chain, + self.MAX_EFFECT_OPTS) torchaudio._audio_normalization(out, self.normalization) diff --git a/torchaudio/transforms.py b/torchaudio/transforms.py index 29da721869..f34f5c468e 100644 --- a/torchaudio/transforms.py +++ b/torchaudio/transforms.py @@ -247,7 +247,7 @@ def forward(self, specgram: Tensor) -> Tensor: # pack batch shape = specgram.size() - specgram = specgram.view(-1, shape[-2], shape[-1]) + specgram = specgram.reshape(-1, shape[-2], shape[-1]) if self.fb.numel() == 0: tmp_fb = F.create_fb_matrix(specgram.size(1), self.f_min, self.f_max, self.n_mels, self.sample_rate) @@ -260,7 +260,7 @@ def forward(self, specgram: Tensor) -> Tensor: mel_specgram = torch.matmul(specgram.transpose(1, 2), self.fb).transpose(1, 2) # unpack batch - mel_specgram = mel_specgram.view(shape[:-2] + mel_specgram.shape[-2:]) + mel_specgram = mel_specgram.reshape(shape[:-2] + mel_specgram.shape[-2:]) return mel_specgram @@ -395,6 +395,8 @@ def __init__(self, pad: int = 0, n_mels: int = 128, window_fn: Callable[..., Tensor] = torch.hann_window, + power: Optional[float] = 2., + normalized: bool = False, wkwargs: Optional[dict] = None) -> None: super(MelSpectrogram, self).__init__() self.sample_rate = sample_rate @@ -402,13 +404,15 @@ def __init__(self, self.win_length = win_length if win_length is not None else n_fft self.hop_length = hop_length if hop_length is not None else self.win_length // 2 self.pad = pad + self.power = power + self.normalized = normalized self.n_mels = n_mels # number of mel frequency bins self.f_max = f_max self.f_min = f_min self.spectrogram = Spectrogram(n_fft=self.n_fft, win_length=self.win_length, hop_length=self.hop_length, - pad=self.pad, window_fn=window_fn, power=2., - normalized=False, wkwargs=wkwargs) + pad=self.pad, window_fn=window_fn, power=self.power, + normalized=self.normalized, wkwargs=wkwargs) self.mel_scale = MelScale(self.n_mels, self.sample_rate, self.f_min, self.f_max, self.n_fft // 2 + 1) def forward(self, waveform: Tensor) -> Tensor: @@ -485,7 +489,7 @@ def forward(self, waveform: Tensor) -> Tensor: # pack batch shape = waveform.size() - waveform = waveform.view(-1, shape[-1]) + waveform = waveform.reshape(-1, shape[-1]) mel_specgram = self.MelSpectrogram(waveform) if self.log_mels: @@ -498,7 +502,7 @@ def forward(self, waveform: Tensor) -> Tensor: mfcc = torch.matmul(mel_specgram.transpose(1, 2), self.dct_mat).transpose(1, 2) # unpack batch - mfcc = mfcc.view(shape[:-1] + mfcc.shape[-2:]) + mfcc = mfcc.reshape(shape[:-1] + mfcc.shape[-2:]) return mfcc @@ -779,6 +783,7 @@ class _AxisMasking(torch.nn.Module): mask_param (int): Maximum possible length of the mask. axis (int): What dimension the mask is applied on. iid_masks (bool): Applies iid masks to each of the examples in the batch dimension. + This option is applicable only when the input tensor is 4D. """ __constants__ = ['mask_param', 'axis', 'iid_masks'] @@ -798,7 +803,6 @@ def forward(self, specgram: Tensor, mask_value: float = 0.) -> Tensor: Returns: Tensor: Masked spectrogram of dimensions (..., freq, time). """ - # if iid_masks flag marked and specgram has a batch dimension if self.iid_masks and specgram.dim() == 4: return F.mask_along_axis_iid(specgram, self.mask_param, mask_value, self.axis + 1) @@ -812,10 +816,10 @@ class FrequencyMasking(_AxisMasking): Args: freq_mask_param (int): maximum possible length of the mask. Indices uniformly sampled from [0, freq_mask_param). - iid_masks (bool, optional): whether to apply the same mask to all - the examples/channels in the batch. (Default: ``False``) + iid_masks (bool, optional): whether to apply different masks to each + example/channel in the batch. (Default: ``False``) + This option is applicable only when the input tensor is 4D. """ - def __init__(self, freq_mask_param: int, iid_masks: bool = False) -> None: super(FrequencyMasking, self).__init__(freq_mask_param, 1, iid_masks) @@ -826,10 +830,10 @@ class TimeMasking(_AxisMasking): Args: time_mask_param (int): maximum possible length of the mask. Indices uniformly sampled from [0, time_mask_param). - iid_masks (bool, optional): whether to apply the same mask to all - the examples/channels in the batch. (Default: ``False``) + iid_masks (bool, optional): whether to apply different masks to each + example/channel in the batch. (Default: ``False``) + This option is applicable only when the input tensor is 4D. """ - def __init__(self, time_mask_param: int, iid_masks: bool = False) -> None: super(TimeMasking, self).__init__(time_mask_param, 2, iid_masks) From ca14c17d107fc5aa8e13ccdcb181d8cf5eb23360 Mon Sep 17 00:00:00 2001 From: Moto Hira Date: Mon, 1 Jun 2020 09:23:36 -0700 Subject: [PATCH 06/19] Remove unnecessary config file from torchaudio Summary: Turned out .use_external_sox is not necessary for building torchaudio in fbcode. Reviewed By: vincentqb Differential Revision: D21792939 fbshipit-source-id: c0fb5173c6533e67114f50ddc8e9425bd129574f --- .use_external_sox | 0 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 .use_external_sox diff --git a/.use_external_sox b/.use_external_sox deleted file mode 100644 index e69de29bb2..0000000000 From 74bf3974d8385f45ba810d9fb04a17760f7218d4 Mon Sep 17 00:00:00 2001 From: Ji Chen Date: Fri, 5 Jun 2020 08:46:10 -0700 Subject: [PATCH 07/19] Import torchaudio 20200605 Summary: import torchaudio 0.5.0 in fbcode using import_audio.sh: Reviewed By: vincentqb Differential Revision: D21884426 fbshipit-source-id: b6f2cc308e597caef2dd767c315b167c09fb0d4c --- README.md | 108 +- .../setup_helpers/build_third_party.sh | 24 +- .../setup_helpers/build_third_party_helper.sh | 118 +- build_tools/setup_helpers/extension.py | 27 +- docs/source/datasets.rst | 8 + docs/source/functional.rst | 10 + packaging/build_wheel.sh | 2 +- packaging/torchaudio/build.sh | 2 +- test/assets/genres/noise/noise.0000.wav | Bin 0 -> 223532 bytes test/assets/kaldi_test_fbank_args.json | 88 ++ test/assets/kaldi_test_mfcc_args.json | 117 ++ test/kaldi_compatibility_impl.py | 46 +- test/test_batch_consistency.py | 6 + test/test_datasets.py | 5 + test/test_sox_compatibility.py | 118 ++ test/test_transforms.py | 3 + test/torchscript_consistency_impl.py | 32 + torchaudio/compliance/kaldi.py | 6 +- torchaudio/datasets/__init__.py | 2 + torchaudio/datasets/gtzan.py | 1054 +++++++++++++++++ torchaudio/functional.py | 193 +++ 21 files changed, 1867 insertions(+), 102 deletions(-) create mode 100644 test/assets/genres/noise/noise.0000.wav create mode 100644 test/assets/kaldi_test_fbank_args.json create mode 100644 test/assets/kaldi_test_mfcc_args.json create mode 100644 torchaudio/datasets/gtzan.py diff --git a/README.md b/README.md index fc3326075a..94f993ddaa 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,9 @@ torchaudio: an audio library for PyTorch ======================================== -[![Build Status](https://travis-ci.org/pytorch/audio.svg?branch=master)](https://travis-ci.org/pytorch/audio) +[![Build Status](https://circleci.com/gh/pytorch/audio.svg?style=svg)](https://app.circleci.com/pipelines/github/pytorch/audio) +[![Coverage](https://codecov.io/gh/pytorch/audio/branch/master/graph/badge.svg)](https://codecov.io/gh/pytorch/audio) +[![Documentation](https://img.shields.io/badge/dynamic/json.svg?label=docs&url=https%3A%2F%2Fpypi.org%2Fpypi%2Ftorchaudio%2Fjson&query=%24.info.version&colorB=brightgreen&prefix=v)](https://pytorch.org/audio/) The aim of torchaudio is to apply [PyTorch](https://github.com/pytorch/pytorch) to the audio domain. By supporting PyTorch, torchaudio follows the same philosophy @@ -26,30 +28,26 @@ to use and feel like a natural extension. Dependencies ------------ -* pytorch (nightly version needed for development) -* libsox v14.3.2 or above +* PyTorch (See below for the compatible versions) +* libsox v14.3.2 or above (only required when building from source) * [optional] vesis84/kaldi-io-for-python commit cb46cb1f44318a5d04d4941cf39084c5b021241e or above -Quick install on -OSX (Homebrew): -```bash -brew install sox -``` -Linux (Ubuntu): -```bash -sudo apt-get install sox libsox-dev libsox-fmt-all -``` -Anaconda -```bash -conda install -c conda-forge sox -``` +The following is the corresponding ``torchaudio`` versions and supported Python versions. + +| ``torch`` | ``torchaudio`` | ``python`` | +| ------------------------ | ------------------------ | ------------------------------- | +| ``master`` / ``nightly`` | ``master`` / ``nightly`` | ``>=3.6`` | +| ``1.5.0`` | ``0.5.0`` | ``>=3.5`` | +| ``1.4.0`` | ``0.4.0`` | ``==2.7``, ``>=3.5``, ``<=3.8`` | + Installation ------------ -### Binaries +### Binary Distibutions To install the latest version using anaconda, run: + ``` conda install -c pytorch torchaudio ``` @@ -64,26 +62,48 @@ pip install torchaudio -f https://download.pytorch.org/whl/torch_stable.html torch from PyPI. If you need a different torch configuration, preinstall torch before running this command.) -At the moment, there is no automated nightly build process, but we occasionally -build nightlies based on PyTorch nightlies by hand following the instructions in -[packaging](packaging). To install the latest nightly via pip, run: +### Nightly build + +Note that nightly build is build on PyTorch's nightly build. Therefore, you need to install the latest PyTorch when you use nightly build of torchaudio. + +**pip** ``` pip install numpy pip install --pre torchaudio -f https://download.pytorch.org/whl/nightly/torch_nightly.html ``` -To install the latest nightly via conda, run: +**conda** ``` conda install -y -c pytorch-nightly torchaudio ``` - ### From Source If your system configuration is not among the supported configurations -above, you can build from source. +above, you can build torchaudio from source. + +This will require libsox v14.3.2 or above. + +
Click here for the examples on how to install SoX + +OSX (Homebrew): +```bash +brew install sox +``` + +Linux (Ubuntu): +```bash +sudo apt-get install sox libsox-dev libsox-fmt-all +``` + +Anaconda +```bash +conda install -c conda-forge sox +``` + +
```bash # Linux @@ -93,6 +113,48 @@ python setup.py install MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py install ``` +Alternatively, the build process can build SoX (and codecs such as libmad, lame and flac) statically and torchaudio can link them, by setting environment variable `BUILD_SOX=1`. +The build process will fetch and build SoX, liblame, libmad, flac before building extension. + +```bash +# Linux +BUILD_SOX=1 python setup.py install + +# OSX +BUILD_SOX=1 MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py install +``` + +This is known to work on linux and unix distributions such as Ubuntu and CentOS 7 and macOS. +If you try this on a new system and found a solution to make it work, feel free to share it by opening and issue. + +#### Troubleshooting + +
checking build system type... ./config.guess: unable to guess system type + +Since the configuration file for codecs are old, they cannot correctly detect the new environments, such as Jetson Aarch. You need to replace the `config.guess` file in `./third_party/tmp/lame-3.99.5/config.guess` and/or `./third_party/tmp/libmad-0.15.1b/config.guess` with [the latest one](https://github.com/gcc-mirror/gcc/blob/master/config.guess). + +See also: [#658](https://github.com/pytorch/audio/issues/658) + +
+ +
Undefined reference to `tgetnum' when using `BUILD_SOX` + +If while building from within an anaconda environment you come across errors similar to the following: + +``` +../bin/ld: console.c:(.text+0xc1): undefined reference to `tgetnum' +``` + +Install `ncurses` from `conda-forge` before running `python setup.py install`: + +``` +# Install ncurses from conda-forge +conda install -c conda-forge ncurses +``` + +
+ + Quick Usage ----------- diff --git a/build_tools/setup_helpers/build_third_party.sh b/build_tools/setup_helpers/build_third_party.sh index c5e8f8115d..9577776cc9 100755 --- a/build_tools/setup_helpers/build_third_party.sh +++ b/build_tools/setup_helpers/build_third_party.sh @@ -1,5 +1,6 @@ #!/bin/bash -# Build third party libraries in `/third_party/build` or in `/third_party/build`. +# Build third party libraries (SoX, lame, libmad, and flac) +# Usage: ./build_thid_parth.sh [prefix] [download_only?=false] set -e @@ -10,6 +11,7 @@ prefix="${1:-}" if [ -z "${prefix}" ]; then prefix="${root_dir}" fi +download_only="${2:-false}" tp_dir="${prefix}/third_party" tmp_dir="${tp_dir}/tmp" @@ -20,17 +22,29 @@ mkdir -p "${tmp_dir}" "${build_dir}" . "${this_dir}/build_third_party_helper.sh" if ! found_lame "${build_dir}" ; then - build_lame "${tmp_dir}" "${build_dir}" + get_lame "${tmp_dir}" + if [ "${download_only}" = "false" ]; then + build_lame "${tmp_dir}" "${build_dir}" + fi fi if ! found_flac "${build_dir}" ; then - build_flac "${tmp_dir}" "${build_dir}" + get_flac "${tmp_dir}" + if [ "${download_only}" = "false" ]; then + build_flac "${tmp_dir}" "${build_dir}" + fi fi if ! found_mad "${build_dir}" ; then - build_mad "${tmp_dir}" "${build_dir}" + get_mad "${tmp_dir}" + if [ "${download_only}" = "false" ]; then + build_mad "${tmp_dir}" "${build_dir}" + fi fi if ! found_sox "${build_dir}" ; then - build_sox "${tmp_dir}" "${build_dir}" + get_sox "${tmp_dir}" + if [ "${download_only}" = "false" ]; then + build_sox "${tmp_dir}" "${build_dir}" + fi fi diff --git a/build_tools/setup_helpers/build_third_party_helper.sh b/build_tools/setup_helpers/build_third_party_helper.sh index a31fafe718..7cca812409 100644 --- a/build_tools/setup_helpers/build_third_party_helper.sh +++ b/build_tools/setup_helpers/build_third_party_helper.sh @@ -57,23 +57,34 @@ found_sox() { all_found "$1" 'include/sox.h' 'lib/libsox.a' } -build_lame() { +LAME="lame-3.99.5" +LAME_ARCHIVE="${LAME}.tar.gz" + +get_lame() { work_dir="$1" - install_dir="$2" - package="lame-3.99.5" - url="https://downloads.sourceforge.net/project/lame/lame/3.99/lame-3.99.5.tar.gz" + url="https://downloads.sourceforge.net/project/lame/lame/3.99/${LAME_ARCHIVE}" ( cd "${work_dir}" - if [ ! -d "${package}" ]; then - if [ ! -f "${package}.tar.gz" ]; then + if [ ! -d "${LAME}" ]; then + if [ ! -f "${LAME_ARCHIVE}" ]; then printf "Fetching liblame from %s\n" "${url}" - curl $CURL_OPTS -o "${package}.tar.gz" "${url}" + curl $CURL_OPTS -O "${url}" fi - tar xfp "${package}.tar.gz" fi + ) +} + +build_lame() { + work_dir="$1" + install_dir="$2" + ( + cd "${work_dir}" + if [ ! -d "${LAME}" ]; then + tar xfp "${LAME_ARCHIVE}" + fi + cd "${LAME}" # build statically printf "Building liblame\n" - cd "${package}" if [ ! -f Makefile ]; then ./configure ${CONFIG_OPTS} \ --disable-shared --enable-static --prefix="${install_dir}" CFLAGS=-fPIC CXXFLAGS=-fPIC \ @@ -84,23 +95,34 @@ build_lame() { ) } -build_flac() { +FLAC="flac-1.3.2" +FLAC_ARCHIVE="${FLAC}.tar.xz" + +get_flac() { work_dir="$1" - install_dir="$2" - package="flac-1.3.2" - url="https://downloads.sourceforge.net/project/flac/flac-src/flac-1.3.2.tar.xz" + url="https://downloads.sourceforge.net/project/flac/flac-src/${FLAC_ARCHIVE}" ( cd "${work_dir}" - if [ ! -d "${package}" ]; then - if [ ! -f "${package}.tar.xz" ]; then + if [ ! -d "${FLAC}" ]; then + if [ ! -f "${FLAC_ARCHIVE}" ]; then printf "Fetching flac from %s\n" "${url}" - curl $CURL_OPTS -o "${package}.tar.xz" "${url}" + curl $CURL_OPTS -O "${url}" fi - tar xfp "${package}.tar.xz" fi + ) +} + +build_flac() { + work_dir="$1" + install_dir="$2" + ( + cd "${work_dir}" + if [ ! -d "${FLAC}" ]; then + tar xfp "${FLAC_ARCHIVE}" + fi + cd "${FLAC}" # build statically printf "Building flac\n" - cd "${package}" if [ ! -f Makefile ]; then ./configure ${CONFIG_OPTS} \ --disable-shared --enable-static --prefix="${install_dir}" CFLAGS=-fPIC CXXFLAGS=-fPIC \ @@ -111,23 +133,34 @@ build_flac() { ) } -build_mad() { +LIBMAD="libmad-0.15.1b" +LIBMAD_ARCHIVE="${LIBMAD}.tar.gz" + +get_mad() { work_dir="$1" - install_dir="$2" - package="libmad-0.15.1b" - url="https://downloads.sourceforge.net/project/mad/libmad/0.15.1b/libmad-0.15.1b.tar.gz" + url="https://downloads.sourceforge.net/project/mad/libmad/0.15.1b/${LIBMAD_ARCHIVE}" ( cd "${work_dir}" - if [ ! -d "${package}" ]; then - if [ ! -f "${package}.tar.gz" ]; then + if [ ! -d "${LIBMAD}" ]; then + if [ ! -f "${LIBMAD_ARCHIVE}" ]; then printf "Fetching mad from %s\n" "${url}" - curl $CURL_OPTS -o "${package}.tar.gz" "${url}" + curl $CURL_OPTS -O "${url}" fi - tar xfp "${package}.tar.gz" fi + ) +} + +build_mad() { + work_dir="$1" + install_dir="$2" + ( + cd "${work_dir}" + if [ ! -d "${LIBMAD}" ]; then + tar xfp "${LIBMAD_ARCHIVE}" + fi + cd "${LIBMAD}" # build statically printf "Building mad\n" - cd "${package}" if [ ! -f Makefile ]; then # See https://stackoverflow.com/a/12864879/23845 sed -i.bak 's/-march=i486//' configure @@ -140,23 +173,34 @@ build_mad() { ) } -build_sox() { +SOX="sox-14.4.2" +SOX_ARCHIVE="${SOX}.tar.bz2" + +get_sox() { work_dir="$1" - install_dir="$2" - package="sox-14.4.2" - url="https://downloads.sourceforge.net/project/sox/sox/14.4.2/sox-14.4.2.tar.bz2" + url="https://downloads.sourceforge.net/project/sox/sox/14.4.2/${SOX_ARCHIVE}" ( cd "${work_dir}" - if [ ! -d "${package}" ]; then - if [ ! -f "${package}.tar.bz2" ]; then + if [ ! -d "${SOX}" ]; then + if [ ! -f "${SOX_ARCHIVE}" ]; then printf "Fetching SoX from %s\n" "${url}" - curl $CURL_OPTS -o "${package}.tar.bz2" "${url}" + curl $CURL_OPTS -O "${url}" fi - tar xfp "${package}.tar.bz2" fi + ) +} + +build_sox() { + work_dir="$1" + install_dir="$2" + ( + cd "${work_dir}" + if [ ! -d "${SOX}" ]; then + tar xfp "${SOX_ARCHIVE}" + fi + cd "${SOX}" # build statically - printf "Building sox\n" - cd "${package}" + printf "Building SoX\n" if [ ! -f Makefile ]; then # --without-png makes OS X build less hazardous; somehow the build # finds png and enables it. We don't want it; we'd need to package diff --git a/build_tools/setup_helpers/extension.py b/build_tools/setup_helpers/extension.py index 65e48dfc10..dcf61ab259 100644 --- a/build_tools/setup_helpers/extension.py +++ b/build_tools/setup_helpers/extension.py @@ -1,3 +1,4 @@ +import os import platform import subprocess from pathlib import Path @@ -18,9 +19,21 @@ _TP_BASE_DIR = _ROOT_DIR / 'third_party' _TP_INSTALL_DIR = _TP_BASE_DIR / 'build' -# Temporary fix for building in fbcode -# at the moment, we have to use external sox in fbcode -_BUILD_DEPS = not (_ROOT_DIR / '.use_external_sox').exists() + +def _get_build_sox(): + val = os.environ.get('BUILD_SOX', '0') + trues = ['1', 'true', 'TRUE', 'on', 'ON', 'yes', 'YES'] + falses = ['0', 'false', 'FALSE', 'off', 'OFF', 'no', 'NO'] + if val in trues: + return True + if val not in falses: + print( + f'WARNING: Unexpected environment variable value `BUILD_SOX={val}`. ' + f'Expected one of {trues + falses}') + return False + + +_BUILD_SOX = _get_build_sox() def _get_eca(debug): @@ -52,14 +65,14 @@ def _get_include_dirs(): dirs = [ str(_ROOT_DIR), ] - if _BUILD_DEPS: + if _BUILD_SOX: dirs.append(str(_TP_INSTALL_DIR / 'include')) return dirs def _get_extra_objects(): objs = [] - if _BUILD_DEPS: + if _BUILD_SOX: # NOTE: The order of the library listed bellow matters. # # (the most important thing is that dependencies come after a library @@ -71,7 +84,7 @@ def _get_extra_objects(): def _get_libraries(): - return [] if _BUILD_DEPS else ['sox'] + return [] if _BUILD_SOX else ['sox'] def _build_codecs(): @@ -106,6 +119,6 @@ def get_ext_modules(debug=False): class BuildExtension(TorchBuildExtension): def build_extension(self, ext): - if ext.name == _EXT_NAME and _BUILD_DEPS: + if ext.name == _EXT_NAME and _BUILD_SOX: _configure_third_party() super().build_extension(ext) diff --git a/docs/source/datasets.rst b/docs/source/datasets.rst index 27d4b20a34..8ba05dc670 100644 --- a/docs/source/datasets.rst +++ b/docs/source/datasets.rst @@ -33,6 +33,14 @@ COMMONVOICE :special-members: +GTZAN +~~~~~ + +.. autoclass:: GTZAN + :members: __getitem__ + :special-members: + + LIBRISPEECH ~~~~~~~~~~~ diff --git a/docs/source/functional.rst b/docs/source/functional.rst index 5f68666539..12ee165060 100644 --- a/docs/source/functional.rst +++ b/docs/source/functional.rst @@ -113,6 +113,11 @@ Functions to perform common audio operations. .. autofunction:: treble_biquad +:hidden:`bass_biquad` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autofunction:: bass_biquad + :hidden:`deemph_biquad` ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -143,6 +148,11 @@ Functions to perform common audio operations. .. autofunction:: phaser +:hidden:`flanger` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autofunction:: flanger + :hidden:`mask_along_axis` ~~~~~~~~~~~~~~~~~~~~~~~~~ diff --git a/packaging/build_wheel.sh b/packaging/build_wheel.sh index 73cec89ff5..d08196cb20 100755 --- a/packaging/build_wheel.sh +++ b/packaging/build_wheel.sh @@ -15,5 +15,5 @@ if [[ "$OSTYPE" == "msys" ]]; then python_tag="$(echo "cp$PYTHON_VERSION" | tr -d '.')" python setup.py bdist_wheel --plat-name win_amd64 --python-tag $python_tag else - python setup.py bdist_wheel + BUILD_SOX=1 python setup.py bdist_wheel fi diff --git a/packaging/torchaudio/build.sh b/packaging/torchaudio/build.sh index e2b53ddcf4..99c17b6913 100644 --- a/packaging/torchaudio/build.sh +++ b/packaging/torchaudio/build.sh @@ -1,4 +1,4 @@ #!/usr/bin/env bash set -ex -python setup.py install --single-version-externally-managed --record=record.txt +BUILD_SOX=1 python setup.py install --single-version-externally-managed --record=record.txt diff --git a/test/assets/genres/noise/noise.0000.wav b/test/assets/genres/noise/noise.0000.wav new file mode 100644 index 0000000000000000000000000000000000000000..cf1cbcde22e41b56c4a02a8a5b13dbedd5dc3b3a GIT binary patch literal 223532 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b/test/assets/kaldi_test_mfcc_args.json @@ -0,0 +1,117 @@ +{"blackman_coeff": 0.0063, "energy_floor": 3.0323, "frame_length": 0.625, "frame_shift": 0.0625, "high_freq": 5842, "htk_compat": false, "low_freq": 3240, "num_mel_bins": 4, "preemphasis_coefficient": 0.29, "raw_energy": true, "remove_dc_offset": false, "round_to_power_of_two": true, "snip_edges": true, "subtract_mean": true, "use_energy": true, "num_ceps": 2, "cepstral_lifter": 73.5792, "vtln_high": 5749, "vtln_low": 4449, "vtln_warp": 1.0, "window_type": "blackman", "dither": 0.0} +{"blackman_coeff": 0.013, "energy_floor": 1.8509, "frame_length": 1.1875, "frame_shift": 0.625, "high_freq": 7999, "htk_compat": false, "low_freq": 4330, "num_mel_bins": 5, "preemphasis_coefficient": 0.38, "raw_energy": true, "remove_dc_offset": false, "round_to_power_of_two": true, "snip_edges": false, "subtract_mean": false, "use_energy": true, "num_ceps": 3, "cepstral_lifter": 8.1048, "vtln_high": 7497, "vtln_low": 7397, "vtln_warp": 1.0, 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"use_energy": true, "num_ceps": 4, "cepstral_lifter": 70.0635, "vtln_high": 7277, "vtln_low": 7265, "vtln_warp": 1.0, "window_type": "hamming", "dither": 0.0} +{"blackman_coeff": 4.5663, "energy_floor": 1.127, "frame_length": 1.125, "frame_shift": 0.8125, "high_freq": 6069, "htk_compat": true, "low_freq": 167, "num_mel_bins": 8, "preemphasis_coefficient": 0.68, "raw_energy": false, "remove_dc_offset": false, "round_to_power_of_two": true, "snip_edges": false, "subtract_mean": false, "use_energy": true, "num_ceps": 6, "cepstral_lifter": 1.624, "vtln_high": 2148, "vtln_low": 461, "vtln_warp": 1.0, "window_type": "rectangular", "dither": 0.0} +{"blackman_coeff": 4.5896, "energy_floor": 2.7617, "frame_length": 1.0625, "frame_shift": 0.8125, "high_freq": 3851, "htk_compat": true, "low_freq": 1115, "num_mel_bins": 4, "preemphasis_coefficient": 0.03, "raw_energy": true, "remove_dc_offset": false, "round_to_power_of_two": true, "snip_edges": true, "subtract_mean": false, "use_energy": false, "num_ceps": 4, "cepstral_lifter": 49.7637, "vtln_high": 2897, "vtln_low": 2701, "vtln_warp": 1.0, "window_type": "hanning", "dither": 0.0} +{"blackman_coeff": 4.6128, "energy_floor": 0.1203, "frame_length": 1.1875, "frame_shift": 0.9375, "high_freq": 6901, "htk_compat": false, "low_freq": 3577, "num_mel_bins": 6, "preemphasis_coefficient": 0.25, "raw_energy": false, "remove_dc_offset": false, "round_to_power_of_two": true, "snip_edges": false, "subtract_mean": true, "use_energy": false, "num_ceps": 3, "cepstral_lifter": 70.5509, "vtln_high": 5962, "vtln_low": 4190, "vtln_warp": 1.0, "window_type": "rectangular", "dither": 0.0} +{"blackman_coeff": 4.6262, "energy_floor": 4.1656, "frame_length": 1.1875, "frame_shift": 0.8125, "high_freq": 6147, "htk_compat": false, "low_freq": 1684, "num_mel_bins": 6, "preemphasis_coefficient": 0.58, "raw_energy": true, "remove_dc_offset": false, "round_to_power_of_two": true, "snip_edges": true, "subtract_mean": true, "use_energy": true, "num_ceps": 5, "cepstral_lifter": 54.2056, "vtln_high": 5259, "vtln_low": 2363, "vtln_warp": 1.0, "window_type": "rectangular", "dither": 0.0} +{"blackman_coeff": 4.6741, "energy_floor": 4.3867, "frame_length": 1.125, "frame_shift": 1.125, "high_freq": 6273, "htk_compat": false, "low_freq": 2481, "num_mel_bins": 4, "preemphasis_coefficient": 0.15, "raw_energy": true, "remove_dc_offset": false, "round_to_power_of_two": false, "snip_edges": true, "subtract_mean": false, "use_energy": false, "num_ceps": 4, "cepstral_lifter": 75.6122, "vtln_high": 3701, "vtln_low": 2992, "vtln_warp": 1.0, "window_type": "hamming", "dither": 0.0} +{"blackman_coeff": 4.6765, "energy_floor": 1.2644, "frame_length": 1.125, "frame_shift": 0.75, "high_freq": 5204, "htk_compat": false, "low_freq": 276, "num_mel_bins": 4, "preemphasis_coefficient": 0.04, "raw_energy": true, "remove_dc_offset": false, "round_to_power_of_two": false, "snip_edges": true, "subtract_mean": true, "use_energy": false, "num_ceps": 3, "cepstral_lifter": 96.116, "vtln_high": 5148, "vtln_low": 2541, "vtln_warp": 1.0, "window_type": "hamming", "dither": 0.0} +{"blackman_coeff": 4.7216, "energy_floor": 2.4818, "frame_length": 0.8125, "frame_shift": 0.375, "high_freq": 6723, "htk_compat": true, "low_freq": 2352, "num_mel_bins": 6, "preemphasis_coefficient": 0.14, "raw_energy": false, "remove_dc_offset": false, "round_to_power_of_two": true, "snip_edges": false, "subtract_mean": true, "use_energy": false, "num_ceps": 3, "cepstral_lifter": 32.0303, "vtln_high": 5598, "vtln_low": 2579, "vtln_warp": 1.0, "window_type": "hanning", "dither": 0.0} +{"blackman_coeff": 4.7919, "energy_floor": 2.6435, "frame_length": 0.625, "frame_shift": 0.5, "high_freq": 7971, "htk_compat": false, "low_freq": 1812, "num_mel_bins": 4, "preemphasis_coefficient": 0.65, "raw_energy": false, "remove_dc_offset": false, "round_to_power_of_two": false, "snip_edges": false, "subtract_mean": true, "use_energy": false, "num_ceps": 4, "cepstral_lifter": 27.7648, "vtln_high": 7735, "vtln_low": 7419, "vtln_warp": 1.0, "window_type": "povey", "dither": 0.0} +{"blackman_coeff": 4.814, "energy_floor": 0.468, "frame_length": 1.0625, "frame_shift": 0.6875, "high_freq": 5252, "htk_compat": true, "low_freq": 569, "num_mel_bins": 6, "preemphasis_coefficient": 0.85, "raw_energy": true, "remove_dc_offset": false, "round_to_power_of_two": true, "snip_edges": true, "subtract_mean": false, "use_energy": false, "num_ceps": 6, "cepstral_lifter": 56.449, "vtln_high": 4397, "vtln_low": 4332, "vtln_warp": 1.0, "window_type": "hamming", "dither": 0.0} +{"blackman_coeff": 4.95, "energy_floor": 4.5916, "frame_length": 1.125, "frame_shift": 1.0625, "high_freq": 5044, "htk_compat": true, "low_freq": 617, "num_mel_bins": 8, "preemphasis_coefficient": 0.89, "raw_energy": false, "remove_dc_offset": false, "round_to_power_of_two": false, "snip_edges": false, "subtract_mean": true, "use_energy": true, "num_ceps": 8, "cepstral_lifter": 23.3238, "vtln_high": 2732, "vtln_low": 2677, "vtln_warp": 1.0, "window_type": "hanning", "dither": 0.0} +{"blackman_coeff": 4.9663, "energy_floor": 4.7867, "frame_length": 1.1875, "frame_shift": 0.5, "high_freq": 2424, "htk_compat": false, "low_freq": 350, "num_mel_bins": 4, "preemphasis_coefficient": 0.39, "raw_energy": false, "remove_dc_offset": false, "round_to_power_of_two": true, "snip_edges": false, "subtract_mean": true, "use_energy": true, "num_ceps": 2, "cepstral_lifter": 59.4319, "vtln_high": 1202, "vtln_low": 1063, "vtln_warp": 1.0, "window_type": "hamming", "dither": 0.0} diff --git a/test/kaldi_compatibility_impl.py b/test/kaldi_compatibility_impl.py index 8fbdff75cb..2d3313d007 100644 --- a/test/kaldi_compatibility_impl.py +++ b/test/kaldi_compatibility_impl.py @@ -1,4 +1,5 @@ """Test suites for checking numerical compatibility against Kaldi""" +import json import shutil import unittest import subprocess @@ -9,6 +10,7 @@ import torchaudio.compliance.kaldi from . import common_utils +from parameterized import parameterized, param def _not_available(cmd): @@ -47,6 +49,11 @@ def _run_kaldi(command, input_type, input_value): return torch.from_numpy(result.copy()) # copy supresses some torch warning +def _load_params(path): + with open(path, 'r') as file: + return [param(json.loads(line)) for line in file] + + class Kaldi(common_utils.TestBaseMixin): def assert_equal(self, output, *, expected, rtol=None, atol=None): expected = expected.to(dtype=self.dtype, device=self.device) @@ -68,37 +75,24 @@ def test_sliding_window_cmn(self): kaldi_result = _run_kaldi(command, 'ark', tensor) self.assert_equal(result, expected=kaldi_result) + @parameterized.expand(_load_params(common_utils.get_asset_path('kaldi_test_fbank_args.json'))) @unittest.skipIf(_not_available('compute-fbank-feats'), '`compute-fbank-feats` not available') - def test_fbank(self): + def test_fbank(self, kwargs): """fbank should be numerically compatible with compute-fbank-feats""" - kwargs = { - 'blackman_coeff': 4.3926, - 'dither': 0.0, - 'energy_floor': 2.0617, - 'frame_length': 0.5625, - 'frame_shift': 0.0625, - 'high_freq': 4253, - 'htk_compat': True, - 'low_freq': 1367, - 'num_mel_bins': 5, - 'preemphasis_coefficient': 0.84, - 'raw_energy': False, - 'remove_dc_offset': True, - 'round_to_power_of_two': True, - 'snip_edges': True, - 'subtract_mean': False, - 'use_energy': True, - 'use_log_fbank': True, - 'use_power': False, - 'vtln_high': 2112, - 'vtln_low': 1445, - 'vtln_warp': 1.0000, - 'window_type': 'hamming', - - } wave_file = common_utils.get_asset_path('kaldi_file.wav') waveform = torchaudio.load_wav(wave_file)[0].to(dtype=self.dtype, device=self.device) result = torchaudio.compliance.kaldi.fbank(waveform, **kwargs) command = ['compute-fbank-feats'] + _convert_args(**kwargs) + ['scp:-', 'ark:-'] kaldi_result = _run_kaldi(command, 'scp', wave_file) self.assert_equal(result, expected=kaldi_result, rtol=1e-4, atol=1e-8) + + @parameterized.expand(_load_params(common_utils.get_asset_path('kaldi_test_mfcc_args.json'))) + @unittest.skipIf(_not_available('compute-mfcc-feats'), '`compute-mfcc-feats` not available') + def test_mfcc(self, kwargs): + """mfcc should be numerically compatible with compute-mfcc-feats""" + wave_file = common_utils.get_asset_path('kaldi_file.wav') + waveform = torchaudio.load_wav(wave_file)[0].to(dtype=self.dtype, device=self.device) + result = torchaudio.compliance.kaldi.mfcc(waveform, **kwargs) + command = ['compute-mfcc-feats'] + _convert_args(**kwargs) + ['scp:-', 'ark:-'] + kaldi_result = _run_kaldi(command, 'scp', wave_file) + self.assert_equal(result, expected=kaldi_result, rtol=1e-4, atol=1e-8) diff --git a/test/test_batch_consistency.py b/test/test_batch_consistency.py index c10652d9ca..103d83bb84 100644 --- a/test/test_batch_consistency.py +++ b/test/test_batch_consistency.py @@ -84,6 +84,12 @@ def test_phaser(self): waveform, sample_rate = torchaudio.load(filepath) self.assert_batch_consistencies(F.phaser, waveform, sample_rate) + def test_flanger(self): + torch.random.manual_seed(40) + waveform = torch.rand(2, 100) - 0.5 + sample_rate = 44100 + self.assert_batch_consistencies(F.flanger, waveform, sample_rate) + def test_sliding_window_cmn(self): waveform = torch.randn(2, 1024) - 0.5 self.assert_batch_consistencies(F.sliding_window_cmn, waveform, center=True, norm_vars=True) diff --git a/test/test_datasets.py b/test/test_datasets.py index 96c12884d6..ab1e74e888 100644 --- a/test/test_datasets.py +++ b/test/test_datasets.py @@ -7,6 +7,7 @@ from torchaudio.datasets.vctk import VCTK from torchaudio.datasets.yesno import YESNO from torchaudio.datasets.ljspeech import LJSPEECH +from torchaudio.datasets.gtzan import GTZAN from . import common_utils @@ -55,6 +56,10 @@ def test_speechcommands(self): data = SPEECHCOMMANDS(self.path) data[0] + def test_gtzan(self): + data = GTZAN(self.path) + data[0] + if __name__ == "__main__": unittest.main() diff --git a/test/test_sox_compatibility.py b/test/test_sox_compatibility.py index dfa57cec47..b6e6dbaedd 100644 --- a/test/test_sox_compatibility.py +++ b/test/test_sox_compatibility.py @@ -265,6 +265,28 @@ def test_treble(self): self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") + def test_bass(self): + """ + Test biquad bass filter, compare to SoX implementation + """ + + central_freq = 1000 + q = 0.707 + gain = 40 + + noise_filepath = common_utils.get_asset_path('whitenoise.wav') + E = torchaudio.sox_effects.SoxEffectsChain() + E.set_input_file(noise_filepath) + E.append_effect_to_chain("bass", [gain, central_freq, str(q) + 'q']) + sox_output_waveform, sr = E.sox_build_flow_effects() + + waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) + output_waveform = F.bass_biquad(waveform, sample_rate, gain, central_freq, q) + + self.assertEqual(output_waveform, sox_output_waveform, atol=1.5e-4, rtol=1e-5) + @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") def test_deemph(self): @@ -419,6 +441,102 @@ def test_phaser_triangle(self): self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") + def test_flanger_triangle_linear(self): + """ + Test flanger effect with triangle modulation and linear interpolation, compare to SoX implementation + """ + delay = 0.6 + depth = 0.87 + regen = 3.0 + width = 0.9 + speed = 0.5 + phase = 30 + noise_filepath = common_utils.get_asset_path('whitenoise.wav') + E = torchaudio.sox_effects.SoxEffectsChain() + E.set_input_file(noise_filepath) + E.append_effect_to_chain("flanger", [delay, depth, regen, width, speed, "triangle", phase, "linear"]) + sox_output_waveform, sr = E.sox_build_flow_effects() + + waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) + output_waveform = F.flanger(waveform, sample_rate, delay, depth, regen, width, speed, phase, + modulation='triangular', interpolation='linear') + + torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") + def test_flanger_triangle_quad(self): + """ + Test flanger effect with triangle modulation and quadratic interpolation, compare to SoX implementation + """ + delay = 0.8 + depth = 0.88 + regen = 3.0 + width = 0.4 + speed = 0.5 + phase = 40 + noise_filepath = common_utils.get_asset_path('whitenoise.wav') + E = torchaudio.sox_effects.SoxEffectsChain() + E.set_input_file(noise_filepath) + E.append_effect_to_chain("flanger", [delay, depth, regen, width, speed, "triangle", phase, "quadratic"]) + sox_output_waveform, sr = E.sox_build_flow_effects() + + waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) + output_waveform = F.flanger(waveform, sample_rate, delay, depth, regen, width, speed, phase, + modulation='triangular', interpolation='quadratic') + + torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") + def test_flanger_sine_linear(self): + """ + Test flanger effect with sine modulation and linear interpolation, compare to SoX implementation + """ + delay = 0.8 + depth = 0.88 + regen = 3.0 + width = 0.23 + speed = 1.3 + phase = 60 + noise_filepath = common_utils.get_asset_path('whitenoise.wav') + E = torchaudio.sox_effects.SoxEffectsChain() + E.set_input_file(noise_filepath) + E.append_effect_to_chain("flanger", [delay, depth, regen, width, speed, "sine", phase, "linear"]) + sox_output_waveform, sr = E.sox_build_flow_effects() + + waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) + output_waveform = F.flanger(waveform, sample_rate, delay, depth, regen, width, speed, phase, + modulation='sinusoidal', interpolation='linear') + + torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + + @unittest.skipIf("sox" not in BACKENDS, "sox not available") + @AudioBackendScope("sox") + def test_flanger_sine_quad(self): + """ + Test flanger effect with sine modulation and quadratic interpolation, compare to SoX implementation + """ + delay = 0.9 + depth = 0.9 + regen = 4.0 + width = 0.23 + speed = 1.3 + phase = 25 + noise_filepath = common_utils.get_asset_path('whitenoise.wav') + E = torchaudio.sox_effects.SoxEffectsChain() + E.set_input_file(noise_filepath) + E.append_effect_to_chain("flanger", [delay, depth, regen, width, speed, "sine", phase, "quadratic"]) + sox_output_waveform, sr = E.sox_build_flow_effects() + + waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) + output_waveform = F.flanger(waveform, sample_rate, delay, depth, regen, width, speed, phase, + modulation='sinusoidal', interpolation='quadratic') + + torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + @unittest.skipIf("sox" not in BACKENDS, "sox not available") @AudioBackendScope("sox") def test_equalizer(self): diff --git a/test/test_transforms.py b/test/test_transforms.py index 02faa68992..ad8a55fb4b 100644 --- a/test/test_transforms.py +++ b/test/test_transforms.py @@ -31,7 +31,10 @@ def test_mu_law_companding(self): quantization_channels = 256 waveform = self.waveform.clone() + if not waveform.is_floating_point(): + waveform = waveform.to(torch.get_default_dtype()) waveform /= torch.abs(waveform).max() + self.assertTrue(waveform.min() >= -1. and waveform.max() <= 1.) waveform_mu = transforms.MuLawEncoding(quantization_channels)(waveform) diff --git a/test/torchscript_consistency_impl.py b/test/torchscript_consistency_impl.py index a4d3cc79cf..a84b629bd7 100644 --- a/test/torchscript_consistency_impl.py +++ b/test/torchscript_consistency_impl.py @@ -367,6 +367,21 @@ def func(tensor): self._assert_consistency(func, waveform) + def test_bass(self): + if self.dtype == torch.float64: + raise unittest.SkipTest("This test is known to fail for float64") + + waveform = common_utils.get_whitenoise(sample_rate=44100) + + def func(tensor): + sample_rate = 44100 + gain = 40. + central_freq = 1000. + q = 0.707 + return F.bass_biquad(tensor, sample_rate, gain, central_freq, q) + + self._assert_consistency(func, waveform) + def test_deemph(self): if self.dtype == torch.float64: raise unittest.SkipTest("This test is known to fail for float64") @@ -497,6 +512,23 @@ def func(tensor): self._assert_consistency(func, waveform) + def test_flanger(self): + torch.random.manual_seed(40) + waveform = torch.rand(2, 100) - 0.5 + + def func(tensor): + delay = 0.8 + depth = 0.88 + regen = 3.0 + width = 0.23 + speed = 1.3 + phase = 60. + sample_rate = 44100 + return F.flanger(tensor, sample_rate, delay, depth, regen, width, speed, + phase, modulation='sinusoidal', interpolation='linear') + + self._assert_consistency(func, waveform) + class Transforms(common_utils.TestBaseMixin): """Implements test for Transforms that are performed for different devices""" diff --git a/torchaudio/compliance/kaldi.py b/torchaudio/compliance/kaldi.py index 8115c3a25f..bdf5f407ba 100644 --- a/torchaudio/compliance/kaldi.py +++ b/torchaudio/compliance/kaldi.py @@ -696,6 +696,8 @@ def mfcc( """ assert num_ceps <= num_mel_bins, 'num_ceps cannot be larger than num_mel_bins: %d vs %d' % (num_ceps, num_mel_bins) + device, dtype = waveform.device, waveform.dtype + # The mel_energies should not be squared (use_power=True), not have mean subtracted # (subtract_mean=False), and use log (use_log_fbank=True). # size (m, num_mel_bins + use_energy) @@ -717,7 +719,7 @@ def mfcc( feature = feature[:, mel_offset:(num_mel_bins + mel_offset)] # size (num_mel_bins, num_ceps) - dct_matrix = _get_dct_matrix(num_ceps, num_mel_bins) + dct_matrix = _get_dct_matrix(num_ceps, num_mel_bins).to(dtype=dtype, device=device) # size (m, num_ceps) feature = feature.matmul(dct_matrix) @@ -725,7 +727,7 @@ def mfcc( if cepstral_lifter != 0.0: # size (1, num_ceps) lifter_coeffs = _get_lifter_coeffs(num_ceps, cepstral_lifter).unsqueeze(0) - feature *= lifter_coeffs + feature *= lifter_coeffs.to(device=device, dtype=dtype) # if use_energy then replace the last column for htk_compat == true else first column if use_energy: diff --git a/torchaudio/datasets/__init__.py b/torchaudio/datasets/__init__.py index 26d8216114..1741889029 100644 --- a/torchaudio/datasets/__init__.py +++ b/torchaudio/datasets/__init__.py @@ -3,6 +3,7 @@ from .speechcommands import SPEECHCOMMANDS from .utils import bg_iterator, diskcache_iterator from .vctk import VCTK +from .gtzan import GTZAN from .yesno import YESNO from .ljspeech import LJSPEECH @@ -13,6 +14,7 @@ "VCTK", "YESNO", "LJSPEECH", + "GTZAN", "diskcache_iterator", "bg_iterator", ) diff --git a/torchaudio/datasets/gtzan.py b/torchaudio/datasets/gtzan.py new file mode 100644 index 0000000000..13d7e8b68b --- /dev/null +++ b/torchaudio/datasets/gtzan.py @@ -0,0 +1,1054 @@ +import os +import warnings +from typing import Any, Tuple + +import torchaudio +from torch import Tensor +from torch.utils.data import Dataset +from torchaudio.datasets.utils import ( + download_url, + extract_archive, + walk_files, +) + +# The following lists prefixed with `filtered_` provide a filtered split +# that: +# +# a. Mitigate a known issue with GTZAN (duplication) +# +# b. Provide a standard split for testing it against other +# methods (e.g. the one in jordipons/sklearn-audio-transfer-learning). +# +# Those are used when GTZAN is initialised with the `filtered` keyword. +# The split was taken from (github) jordipons/sklearn-audio-transfer-learning. + +filtered_test = [ + "blues.00012", + "blues.00013", + "blues.00014", + "blues.00015", + "blues.00016", + "blues.00017", + "blues.00018", + "blues.00019", + "blues.00020", + "blues.00021", + "blues.00022", + "blues.00023", + "blues.00024", + "blues.00025", + "blues.00026", + "blues.00027", + "blues.00028", + "blues.00061", + "blues.00062", + "blues.00063", + "blues.00064", + "blues.00065", + "blues.00066", + "blues.00067", + "blues.00068", + "blues.00069", + "blues.00070", + "blues.00071", + "blues.00072", + "blues.00098", + "blues.00099", + "classical.00011", + "classical.00012", + "classical.00013", + "classical.00014", + "classical.00015", + "classical.00016", + "classical.00017", + "classical.00018", + "classical.00019", + "classical.00020", + "classical.00021", + "classical.00022", + "classical.00023", + "classical.00024", + "classical.00025", + "classical.00026", + "classical.00027", + "classical.00028", + "classical.00029", + "classical.00034", + "classical.00035", + "classical.00036", + "classical.00037", + "classical.00038", + "classical.00039", + "classical.00040", + "classical.00041", + "classical.00049", + "classical.00077", + "classical.00078", + "classical.00079", + "country.00030", + "country.00031", + "country.00032", + "country.00033", + "country.00034", + "country.00035", + "country.00036", + "country.00037", + "country.00038", + "country.00039", + "country.00040", + "country.00043", + "country.00044", + "country.00046", + "country.00047", + "country.00048", + "country.00050", + "country.00051", + "country.00053", + "country.00054", + "country.00055", + "country.00056", + "country.00057", + "country.00058", + "country.00059", + "country.00060", + "country.00061", + "country.00062", + "country.00063", + "country.00064", + "disco.00001", + "disco.00021", + "disco.00058", + "disco.00062", + "disco.00063", + "disco.00064", + "disco.00065", + "disco.00066", + "disco.00069", + "disco.00076", + "disco.00077", + "disco.00078", + "disco.00079", + "disco.00080", + "disco.00081", + "disco.00082", + "disco.00083", + "disco.00084", + "disco.00085", + "disco.00086", + "disco.00087", + "disco.00088", + "disco.00091", + "disco.00092", + "disco.00093", + "disco.00094", + "disco.00096", + "disco.00097", + "disco.00099", + "hiphop.00000", + "hiphop.00026", + "hiphop.00027", + "hiphop.00030", + "hiphop.00040", + "hiphop.00043", + "hiphop.00044", + "hiphop.00045", + "hiphop.00051", + "hiphop.00052", + "hiphop.00053", + "hiphop.00054", + "hiphop.00062", + "hiphop.00063", + "hiphop.00064", + "hiphop.00065", + "hiphop.00066", + "hiphop.00067", + "hiphop.00068", + "hiphop.00069", + "hiphop.00070", + "hiphop.00071", + "hiphop.00072", + "hiphop.00073", + "hiphop.00074", + "hiphop.00075", + "hiphop.00099", + "jazz.00073", + "jazz.00074", + "jazz.00075", + "jazz.00076", + "jazz.00077", + "jazz.00078", + "jazz.00079", + "jazz.00080", + "jazz.00081", + "jazz.00082", + "jazz.00083", + "jazz.00084", + "jazz.00085", + "jazz.00086", + "jazz.00087", + "jazz.00088", + "jazz.00089", + "jazz.00090", + "jazz.00091", + "jazz.00092", + "jazz.00093", + "jazz.00094", + "jazz.00095", + "jazz.00096", + "jazz.00097", + "jazz.00098", + "jazz.00099", + "metal.00012", + "metal.00013", + "metal.00014", + "metal.00015", + "metal.00022", + "metal.00023", + "metal.00025", + "metal.00026", + "metal.00027", + "metal.00028", + "metal.00029", + "metal.00030", + "metal.00031", + "metal.00032", + "metal.00033", + "metal.00038", + "metal.00039", + "metal.00067", + "metal.00070", + "metal.00073", + "metal.00074", + "metal.00075", + "metal.00078", + "metal.00083", + "metal.00085", + "metal.00087", + "metal.00088", + "pop.00000", + "pop.00001", + "pop.00013", + "pop.00014", + "pop.00043", + "pop.00063", + "pop.00064", + "pop.00065", + "pop.00066", + "pop.00069", + "pop.00070", + "pop.00071", + "pop.00072", + "pop.00073", + "pop.00074", + "pop.00075", + "pop.00076", + "pop.00077", + "pop.00078", + "pop.00079", + "pop.00082", + "pop.00088", + "pop.00089", + "pop.00090", + "pop.00091", + "pop.00092", + "pop.00093", + "pop.00094", + "pop.00095", + "pop.00096", + "reggae.00034", + "reggae.00035", + "reggae.00036", + "reggae.00037", + "reggae.00038", + "reggae.00039", + "reggae.00040", + "reggae.00046", + "reggae.00047", + "reggae.00048", + "reggae.00052", + "reggae.00053", + "reggae.00064", + "reggae.00065", + "reggae.00066", + "reggae.00067", + "reggae.00068", + "reggae.00071", + "reggae.00079", + "reggae.00082", + "reggae.00083", + "reggae.00084", + "reggae.00087", + "reggae.00088", + "reggae.00089", + "reggae.00090", + "rock.00010", + "rock.00011", + "rock.00012", + "rock.00013", + "rock.00014", + "rock.00015", + "rock.00027", + "rock.00028", + "rock.00029", + "rock.00030", + "rock.00031", + "rock.00032", + "rock.00033", + "rock.00034", + "rock.00035", + "rock.00036", + "rock.00037", + "rock.00039", + "rock.00040", + "rock.00041", + "rock.00042", + "rock.00043", + "rock.00044", + "rock.00045", + "rock.00046", + "rock.00047", + "rock.00048", + "rock.00086", + "rock.00087", + "rock.00088", + "rock.00089", + "rock.00090", +] + +filtered_train = [ + "blues.00029", + "blues.00030", + "blues.00031", + "blues.00032", + "blues.00033", + "blues.00034", + "blues.00035", + "blues.00036", + "blues.00037", + "blues.00038", + "blues.00039", + "blues.00040", + "blues.00041", + "blues.00042", + "blues.00043", + "blues.00044", + "blues.00045", + "blues.00046", + "blues.00047", + "blues.00048", + "blues.00049", + "blues.00073", + "blues.00074", + "blues.00075", + "blues.00076", + "blues.00077", + "blues.00078", + "blues.00079", + "blues.00080", + "blues.00081", + "blues.00082", + "blues.00083", + "blues.00084", + "blues.00085", + "blues.00086", + "blues.00087", + "blues.00088", + "blues.00089", + "blues.00090", + "blues.00091", + "blues.00092", + "blues.00093", + "blues.00094", + "blues.00095", + "blues.00096", + "blues.00097", + "classical.00030", + "classical.00031", + "classical.00032", + "classical.00033", + "classical.00043", + "classical.00044", + "classical.00045", + "classical.00046", + "classical.00047", + "classical.00048", + "classical.00050", + "classical.00051", + "classical.00052", + "classical.00053", + "classical.00054", + "classical.00055", + "classical.00056", + "classical.00057", + "classical.00058", + "classical.00059", + "classical.00060", + "classical.00061", + "classical.00062", + "classical.00063", + "classical.00064", + "classical.00065", + "classical.00066", + "classical.00067", + "classical.00080", + "classical.00081", + "classical.00082", + "classical.00083", + "classical.00084", + "classical.00085", + "classical.00086", + "classical.00087", + "classical.00088", + "classical.00089", + "classical.00090", + "classical.00091", + "classical.00092", + "classical.00093", + "classical.00094", + "classical.00095", + "classical.00096", + "classical.00097", + "classical.00098", + "classical.00099", + "country.00019", + "country.00020", + "country.00021", + "country.00022", + "country.00023", + "country.00024", + "country.00025", + "country.00026", + "country.00028", + "country.00029", + "country.00065", + "country.00066", + "country.00067", + "country.00068", + "country.00069", + "country.00070", + "country.00071", + "country.00072", + "country.00073", + "country.00074", + "country.00075", + "country.00076", + "country.00077", + "country.00078", + "country.00079", + "country.00080", + "country.00081", + "country.00082", + "country.00083", + "country.00084", + "country.00085", + "country.00086", + "country.00087", + "country.00088", + "country.00089", + "country.00090", + "country.00091", + "country.00092", + "country.00093", + "country.00094", + "country.00095", + "country.00096", + "country.00097", + "country.00098", + "country.00099", + "disco.00005", + "disco.00015", + "disco.00016", + "disco.00017", + "disco.00018", + "disco.00019", + "disco.00020", + "disco.00022", + "disco.00023", + "disco.00024", + "disco.00025", + "disco.00026", + "disco.00027", + "disco.00028", + "disco.00029", + "disco.00030", + "disco.00031", + "disco.00032", + "disco.00033", + "disco.00034", + "disco.00035", + "disco.00036", + "disco.00037", + "disco.00039", + "disco.00040", + "disco.00041", + "disco.00042", + "disco.00043", + "disco.00044", + "disco.00045", + "disco.00047", + "disco.00049", + "disco.00053", + "disco.00054", + "disco.00056", + "disco.00057", + "disco.00059", + "disco.00061", + "disco.00070", + "disco.00073", + "disco.00074", + "disco.00089", + "hiphop.00002", + "hiphop.00003", + "hiphop.00004", + "hiphop.00005", + "hiphop.00006", + "hiphop.00007", + "hiphop.00008", + "hiphop.00009", + "hiphop.00010", + "hiphop.00011", + "hiphop.00012", + "hiphop.00013", + "hiphop.00014", + "hiphop.00015", + "hiphop.00016", + "hiphop.00017", + "hiphop.00018", + "hiphop.00019", + "hiphop.00020", + "hiphop.00021", + "hiphop.00022", + "hiphop.00023", + "hiphop.00024", + "hiphop.00025", + "hiphop.00028", + "hiphop.00029", + "hiphop.00031", + "hiphop.00032", + "hiphop.00033", + "hiphop.00034", + "hiphop.00035", + "hiphop.00036", + "hiphop.00037", + "hiphop.00038", + "hiphop.00041", + "hiphop.00042", + "hiphop.00055", + "hiphop.00056", + "hiphop.00057", + "hiphop.00058", + "hiphop.00059", + "hiphop.00060", + "hiphop.00061", + "hiphop.00077", + "hiphop.00078", + "hiphop.00079", + "hiphop.00080", + "jazz.00000", + "jazz.00001", + "jazz.00011", + "jazz.00012", + "jazz.00013", + "jazz.00014", + "jazz.00015", + "jazz.00016", + "jazz.00017", + "jazz.00018", + "jazz.00019", + "jazz.00020", + "jazz.00021", + "jazz.00022", + "jazz.00023", + "jazz.00024", + "jazz.00041", + "jazz.00047", + "jazz.00048", + "jazz.00049", + "jazz.00050", + "jazz.00051", + "jazz.00052", + "jazz.00053", + "jazz.00054", + "jazz.00055", + "jazz.00056", + "jazz.00057", + "jazz.00058", + "jazz.00059", + "jazz.00060", + "jazz.00061", + "jazz.00062", + "jazz.00063", + "jazz.00064", + "jazz.00065", + "jazz.00066", + "jazz.00067", + "jazz.00068", + "jazz.00069", + "jazz.00070", + "jazz.00071", + "jazz.00072", + "metal.00002", + "metal.00003", + "metal.00005", + "metal.00021", + "metal.00024", + "metal.00035", + "metal.00046", + "metal.00047", + "metal.00048", + "metal.00049", + "metal.00050", + "metal.00051", + "metal.00052", + "metal.00053", + "metal.00054", + "metal.00055", + "metal.00056", + "metal.00057", + "metal.00059", + "metal.00060", + "metal.00061", + "metal.00062", + "metal.00063", + "metal.00064", + "metal.00065", + "metal.00066", + "metal.00069", + "metal.00071", + "metal.00072", + "metal.00079", + "metal.00080", + "metal.00084", + "metal.00086", + "metal.00089", + "metal.00090", + "metal.00091", + "metal.00092", + "metal.00093", + "metal.00094", + "metal.00095", + "metal.00096", + "metal.00097", + "metal.00098", + "metal.00099", + "pop.00002", + "pop.00003", + "pop.00004", + "pop.00005", + "pop.00006", + "pop.00007", + "pop.00008", + "pop.00009", + "pop.00011", + "pop.00012", + "pop.00016", + "pop.00017", + "pop.00018", + "pop.00019", + "pop.00020", + "pop.00023", + "pop.00024", + "pop.00025", + "pop.00026", + "pop.00027", + "pop.00028", + "pop.00029", + "pop.00031", + "pop.00032", + "pop.00033", + "pop.00034", + "pop.00035", + "pop.00036", + "pop.00038", + "pop.00039", + "pop.00040", + "pop.00041", + "pop.00042", + "pop.00044", + "pop.00046", + "pop.00049", + "pop.00050", + "pop.00080", + "pop.00097", + "pop.00098", + "pop.00099", + "reggae.00000", + "reggae.00001", + "reggae.00002", + "reggae.00004", + "reggae.00006", + "reggae.00009", + "reggae.00011", + "reggae.00012", + "reggae.00014", + "reggae.00015", + "reggae.00016", + "reggae.00017", + "reggae.00018", + "reggae.00019", + "reggae.00020", + "reggae.00021", + "reggae.00022", + "reggae.00023", + "reggae.00024", + "reggae.00025", + "reggae.00026", + "reggae.00027", + "reggae.00028", + "reggae.00029", + "reggae.00030", + "reggae.00031", + "reggae.00032", + "reggae.00042", + "reggae.00043", + "reggae.00044", + "reggae.00045", + "reggae.00049", + "reggae.00050", + "reggae.00051", + "reggae.00054", + "reggae.00055", + "reggae.00056", + "reggae.00057", + "reggae.00058", + "reggae.00059", + "reggae.00060", + "reggae.00063", + "reggae.00069", + "rock.00000", + "rock.00001", + "rock.00002", + "rock.00003", + "rock.00004", + "rock.00005", + "rock.00006", + "rock.00007", + "rock.00008", + "rock.00009", + "rock.00016", + "rock.00017", + "rock.00018", + "rock.00019", + "rock.00020", + "rock.00021", + "rock.00022", + "rock.00023", + "rock.00024", + "rock.00025", + "rock.00026", + "rock.00057", + "rock.00058", + "rock.00059", + "rock.00060", + "rock.00061", + "rock.00062", + "rock.00063", + "rock.00064", + "rock.00065", + "rock.00066", + "rock.00067", + "rock.00068", + "rock.00069", + "rock.00070", + "rock.00091", + "rock.00092", + "rock.00093", + "rock.00094", + "rock.00095", + "rock.00096", + "rock.00097", + "rock.00098", + "rock.00099", +] + +filtered_valid = [ + "blues.00000", + "blues.00001", + "blues.00002", + "blues.00003", + "blues.00004", + "blues.00005", + "blues.00006", + "blues.00007", + "blues.00008", + "blues.00009", + "blues.00010", + "blues.00011", + "blues.00050", + "blues.00051", + "blues.00052", + "blues.00053", + "blues.00054", + "blues.00055", + "blues.00056", + "blues.00057", + "blues.00058", + "blues.00059", + "blues.00060", + "classical.00000", + "classical.00001", + "classical.00002", + "classical.00003", + "classical.00004", + "classical.00005", + "classical.00006", + "classical.00007", + "classical.00008", + "classical.00009", + "classical.00010", + "classical.00068", + "classical.00069", + "classical.00070", + "classical.00071", + "classical.00072", + "classical.00073", + "classical.00074", + "classical.00075", + "classical.00076", + "country.00000", + "country.00001", + "country.00002", + "country.00003", + "country.00004", + "country.00005", + "country.00006", + "country.00007", + "country.00009", + "country.00010", + "country.00011", + "country.00012", + "country.00013", + "country.00014", + "country.00015", + "country.00016", + "country.00017", + "country.00018", + "country.00027", + "country.00041", + "country.00042", + "country.00045", + "country.00049", + "disco.00000", + "disco.00002", + "disco.00003", + "disco.00004", + "disco.00006", + "disco.00007", + "disco.00008", + "disco.00009", + "disco.00010", + "disco.00011", + "disco.00012", + "disco.00013", + "disco.00014", + "disco.00046", + "disco.00048", + "disco.00052", + "disco.00067", + "disco.00068", + "disco.00072", + "disco.00075", + "disco.00090", + "disco.00095", + "hiphop.00081", + "hiphop.00082", + "hiphop.00083", + "hiphop.00084", + "hiphop.00085", + "hiphop.00086", + "hiphop.00087", + "hiphop.00088", + "hiphop.00089", + "hiphop.00090", + "hiphop.00091", + "hiphop.00092", + "hiphop.00093", + "hiphop.00094", + "hiphop.00095", + "hiphop.00096", + "hiphop.00097", + "hiphop.00098", + "jazz.00002", + "jazz.00003", + "jazz.00004", + "jazz.00005", + "jazz.00006", + "jazz.00007", + "jazz.00008", + "jazz.00009", + "jazz.00010", + "jazz.00025", + "jazz.00026", + "jazz.00027", + "jazz.00028", + "jazz.00029", + "jazz.00030", + "jazz.00031", + "jazz.00032", + "metal.00000", + "metal.00001", + "metal.00006", + "metal.00007", + "metal.00008", + "metal.00009", + "metal.00010", + "metal.00011", + "metal.00016", + "metal.00017", + "metal.00018", + "metal.00019", + "metal.00020", + "metal.00036", + "metal.00037", + "metal.00068", + "metal.00076", + "metal.00077", + "metal.00081", + "metal.00082", + "pop.00010", + "pop.00053", + "pop.00055", + "pop.00058", + "pop.00059", + "pop.00060", + "pop.00061", + "pop.00062", + "pop.00081", + "pop.00083", + "pop.00084", + "pop.00085", + "pop.00086", + "reggae.00061", + "reggae.00062", + "reggae.00070", + "reggae.00072", + "reggae.00074", + "reggae.00076", + "reggae.00077", + "reggae.00078", + "reggae.00085", + "reggae.00092", + "reggae.00093", + "reggae.00094", + "reggae.00095", + "reggae.00096", + "reggae.00097", + "reggae.00098", + "reggae.00099", + "rock.00038", + "rock.00049", + "rock.00050", + "rock.00051", + "rock.00052", + "rock.00053", + "rock.00054", + "rock.00055", + "rock.00056", + "rock.00071", + "rock.00072", + "rock.00073", + "rock.00074", + "rock.00075", + "rock.00076", + "rock.00077", + "rock.00078", + "rock.00079", + "rock.00080", + "rock.00081", + "rock.00082", + "rock.00083", + "rock.00084", + "rock.00085", +] + + +URL = "http://opihi.cs.uvic.ca/sound/genres.tar.gz" +FOLDER_IN_ARCHIVE = "genres" +_CHECKSUMS = {"http://opihi.cs.uvic.ca/sound/genres.tar.gz": "5b3d6dddb579ab49814ab86dba69e7c7"} + + +def load_gtzan_item(fileid: str, path: str, ext_audio: str) -> Tuple[Tensor, str]: + """ + Loads a file from the dataset and returns the raw waveform + as a Torch Tensor, its sample rate as an integer, and its + genre as a string. + """ + # Filenames are of the form label.id, e.g. blues.00078 + label, _ = fileid.split(".") + + # Read wav + file_audio = os.path.join(path, label, fileid + ext_audio) + waveform, sample_rate = torchaudio.load(file_audio) + + return waveform, sample_rate, label + + +class GTZAN(Dataset): + """ + Create a Dataset for GTZAN. Each item is a tuple of the form: + waveform, sample_rate, label. + + Please see http://marsyas.info/downloads/datasets.html + if you are planning to use this dataset to publish results. + """ + + _ext_audio = ".wav" + + def __init__( + self, + root: str, + url: str = URL, + folder_in_archive: str = FOLDER_IN_ARCHIVE, + download: bool = False, + subset: Any = None, + ) -> None: + + # super(GTZAN, self).__init__() + self.root = root + self.url = url + self.folder_in_archive = folder_in_archive + self.download = download + self.subset = subset + + assert subset is None or subset in ["training", "validation", "testing"], ( + "When `subset` not None, it must take a value from " + + "{'training', 'validation', 'testing'}." + ) + + archive = os.path.basename(url) + archive = os.path.join(root, archive) + self._path = os.path.join(root, folder_in_archive) + + if download: + if not os.path.isdir(self._path): + if not os.path.isfile(archive): + checksum = _CHECKSUMS.get(url, None) + download_url(url, root, hash_value=checksum, hash_type="md5") + extract_archive(archive) + + if not os.path.isdir(self._path): + raise RuntimeError( + "Dataset not found. Please use `download=True` to download it." + ) + + if self.subset is None: + walker = walk_files( + self._path, suffix=self._ext_audio, prefix=False, remove_suffix=True + ) + self._walker = list(walker) + else: + if self.subset == "training": + self._walker = filtered_train + elif self.subset == "validation": + self._walker = filtered_valid + elif self.subset == "testing": + self._walker = filtered_test + + def __getitem__(self, n: int) -> Tuple[Tensor, int, str]: + fileid = self._walker[n] + item = load_gtzan_item(fileid, self._path, self._ext_audio) + waveform, sample_rate, label = item + return waveform, sample_rate, label + + def __len__(self) -> int: + return len(self._walker) diff --git a/torchaudio/functional.py b/torchaudio/functional.py index f59c46eb14..92580e1d9e 100644 --- a/torchaudio/functional.py +++ b/torchaudio/functional.py @@ -29,6 +29,7 @@ "equalizer_biquad", "band_biquad", "treble_biquad", + "bass_biquad", "deemph_biquad", "riaa_biquad", "biquad", @@ -36,6 +37,7 @@ "dcshift", "overdrive", "phaser", + "flanger", 'mask_along_axis', 'mask_along_axis_iid', 'sliding_window_cmn', @@ -987,6 +989,47 @@ def treble_biquad( return biquad(waveform, b0, b1, b2, a0, a1, a2) +def bass_biquad( + waveform: Tensor, + sample_rate: int, + gain: float, + central_freq: float = 100, + Q: float = 0.707 +) -> Tensor: + r"""Design a bass tone-control effect. Similar to SoX implementation. + + Args: + waveform (Tensor): audio waveform of dimension of `(..., time)` + sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz) + gain (float): desired gain at the boost (or attenuation) in dB. + central_freq (float, optional): central frequency (in Hz). (Default: ``100``) + Q (float, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``). + + Returns: + Tensor: Waveform of dimension of `(..., time)` + + References: + http://sox.sourceforge.net/sox.html + https://www.w3.org/2011/audio/audio-eq-cookbook.html#APF + """ + w0 = 2 * math.pi * central_freq / sample_rate + alpha = math.sin(w0) / 2 / Q + A = math.exp(gain / 40 * math.log(10)) + + temp1 = 2 * math.sqrt(A) * alpha + temp2 = (A - 1) * math.cos(w0) + temp3 = (A + 1) * math.cos(w0) + + b0 = A * ((A + 1) - temp2 + temp1) + b1 = 2 * A * ((A - 1) - temp3) + b2 = A * ((A + 1) - temp2 - temp1) + a0 = (A + 1) + temp2 + temp1 + a1 = -2 * ((A - 1) + temp3) + a2 = (A + 1) + temp2 - temp1 + + return biquad(waveform, b0 / a0, b1 / a0, b2 / a0, a0 / a0, a1 / a0, a2 / a0) + + def deemph_biquad( waveform: Tensor, sample_rate: int @@ -1357,6 +1400,156 @@ def _generate_wave_table( return d +def flanger( + waveform: Tensor, + sample_rate: int, + delay: float = 0., + depth: float = 2., + regen: float = 0., + width: float = 71., + speed: float = 0.5, + phase: float = 25., + modulation: str = 'sinusoidal', + interpolation: str = 'linear' +) -> Tensor: + r"""Apply a flanger effect to the audio. Similar to SoX implementation. + + Args: + waveform (Tensor): audio waveform of dimension of `(..., channel, time)` . + Max 4 channels allowed + sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz) + delay (float): desired delay in milliseconds(ms) + Allowed range of values are 0 to 30 + depth (float): desired delay depth in milliseconds(ms) + Allowed range of values are 0 to 10 + regen (float): desired regen(feeback gain) in dB + Allowed range of values are -95 to 95 + width (float): desired width(delay gain) in dB + Allowed range of values are 0 to 100 + speed (float): modulation speed in Hz + Allowed range of values are 0.1 to 10 + phase (float): percentage phase-shift for multi-channel + Allowed range of values are 0 to 100 + modulation (str): Use either "sinusoidal" or "triangular" modulation. (Default: ``sinusoidal``) + interpolation (str): Use either "linear" or "quadratic" for delay-line interpolation. (Default: ``linear``) + + Returns: + Tensor: Waveform of dimension of `(..., channel, time)` + + References: + http://sox.sourceforge.net/sox.html + + Scott Lehman, Effects Explained, + https://web.archive.org/web/20051125072557/http://www.harmony-central.com/Effects/effects-explained.html + """ + + if modulation not in ('sinusoidal', 'triangular'): + raise ValueError("Only 'sinusoidal' or 'triangular' modulation allowed") + + if interpolation not in ('linear', 'quadratic'): + raise ValueError("Only 'linear' or 'quadratic' interpolation allowed") + + actual_shape = waveform.shape + device, dtype = waveform.device, waveform.dtype + + if actual_shape[-2] > 4: + raise ValueError("Max 4 channels allowed") + + # convert to 3D (batch, channels, time) + waveform = waveform.view(-1, actual_shape[-2], actual_shape[-1]) + + # Scaling + feedback_gain = regen / 100 + delay_gain = width / 100 + channel_phase = phase / 100 + delay_min = delay / 1000 + delay_depth = depth / 1000 + + n_channels = waveform.shape[-2] + + if modulation == 'sinusoidal': + wave_type = 'SINE' + else: + wave_type = 'TRIANGLE' + + # Balance output: + in_gain = 1. / (1 + delay_gain) + delay_gain = delay_gain / (1 + delay_gain) + + # Balance feedback loop: + delay_gain = delay_gain * (1 - abs(feedback_gain)) + + delay_buf_length = int((delay_min + delay_depth) * sample_rate + 0.5) + delay_buf_length = delay_buf_length + 2 + + delay_bufs = torch.zeros(waveform.shape[0], n_channels, delay_buf_length, dtype=dtype, device=device) + delay_last = torch.zeros(waveform.shape[0], n_channels, dtype=dtype, device=device) + + lfo_length = int(sample_rate / speed) + + lfo = torch.zeros(lfo_length, dtype=dtype, device=device) + + table_min = math.floor(delay_min * sample_rate + 0.5) + table_max = delay_buf_length - 2. + + lfo = _generate_wave_table(wave_type=wave_type, + data_type='FLOAT', + table_size=lfo_length, + min=float(table_min), + max=float(table_max), + phase=3 * math.pi / 2) + + output_waveform = torch.zeros_like(waveform, dtype=dtype, device=device) + + delay_buf_pos = 0 + lfo_pos = 0 + channel_idxs = torch.arange(0, n_channels) + + for i in range(waveform.shape[-1]): + + delay_buf_pos = (delay_buf_pos + delay_buf_length - 1) % delay_buf_length + + cur_channel_phase = (channel_idxs * lfo_length * channel_phase + .5).to(torch.int64) + delay_tensor = lfo[(lfo_pos + cur_channel_phase) % lfo_length] + frac_delay = torch.frac(delay_tensor) + delay_tensor = torch.floor(delay_tensor) + + int_delay = delay_tensor.to(torch.int64) + + temp = waveform[:, :, i] + + delay_bufs[:, :, delay_buf_pos] = temp + delay_last * feedback_gain + + delayed_0 = delay_bufs[:, channel_idxs, (delay_buf_pos + int_delay) % delay_buf_length] + + int_delay = int_delay + 1 + + delayed_1 = delay_bufs[:, channel_idxs, (delay_buf_pos + int_delay) % delay_buf_length] + + int_delay = int_delay + 1 + + if interpolation == 'linear': + delayed = delayed_0 + (delayed_1 - delayed_0) * frac_delay + else: + delayed_2 = delay_bufs[:, channel_idxs, (delay_buf_pos + int_delay) % delay_buf_length] + + int_delay = int_delay + 1 + + delayed_2 = delayed_2 - delayed_0 + delayed_1 = delayed_1 - delayed_0 + a = delayed_2 * .5 - delayed_1 + b = delayed_1 * 2 - delayed_2 * .5 + + delayed = delayed_0 + (a * frac_delay + b) * frac_delay + + delay_last = delayed + output_waveform[:, :, i] = waveform[:, :, i] * in_gain + delayed * delay_gain + + lfo_pos = (lfo_pos + 1) % lfo_length + + return output_waveform.clamp(min=-1, max=1).view(actual_shape) + + def mask_along_axis_iid( specgrams: Tensor, mask_param: int, From 0af364402e4764a60e8da0874d3434925d5f1ca7 Mon Sep 17 00:00:00 2001 From: Ben Mehne Date: Wed, 10 Jun 2020 10:46:43 -0700 Subject: [PATCH 08/19] Change parameterized testing system to be compatible with unittest Summary: The previous implementation of parameterized testing worked by modifying test.common_utils inplace. This doesn't work in general because unittest's contract with test modules is such that it must be able to load the module and run the test itself. Because the previous implementation needed to load the module and modify it, it is incompatible. Reviewed By: mthrok Differential Revision: D21964676 fbshipit-source-id: 9bb71e8c3f9fab074239b22306f3bbddb0f3975b --- test/common_utils.py | 9 +++++++++ test/torchscript_consistency_cpu_test.py | 13 +++++++++++-- 2 files changed, 20 insertions(+), 2 deletions(-) diff --git a/test/common_utils.py b/test/common_utils.py index 5d1dc8abe1..00d10a6df1 100644 --- a/test/common_utils.py +++ b/test/common_utils.py @@ -119,6 +119,15 @@ def define_test_suites( scope[t.__name__] = t +def common_test_class_parameters( + dtypes: Iterable[str] = ("float32", "float64"), + devices: Iterable[str] = ("cpu", "cuda"), +): + for device in devices: + for dtype in dtypes: + yield {"device": torch.device(device), "dtype": getattr(torch, dtype)} + + def get_whitenoise( *, sample_rate: int = 16000, diff --git a/test/torchscript_consistency_cpu_test.py b/test/torchscript_consistency_cpu_test.py index 51a3a477f0..81e6616821 100644 --- a/test/torchscript_consistency_cpu_test.py +++ b/test/torchscript_consistency_cpu_test.py @@ -1,5 +1,14 @@ -from .common_utils import define_test_suites +from parameterized import parameterized_class + +from .common_utils import TestCase, common_test_class_parameters from .torchscript_consistency_impl import Functional, Transforms +parameters = list(common_test_class_parameters(devices=['cpu'])) +@parameterized_class(parameters) +class TestFunctional(Functional, TestCase): + pass + -define_test_suites(globals(), [Functional, Transforms], devices=['cpu']) +@parameterized_class(parameters) +class TestTransforms(Transforms, TestCase): + pass From 9a2d018f8356ea93542990079755ad64d2bde15c Mon Sep 17 00:00:00 2001 From: Moto Hira Date: Mon, 22 Jun 2020 16:41:11 -0700 Subject: [PATCH 09/19] Import torchaudio 20200618 #718 Summary: Import torchaudio up to #719 Reviewed By: zhangguanheng66 Differential Revision: D22119491 fbshipit-source-id: e14842278a32c9373179fc132e8111a0ffe66d93 --- docs/source/datasets.rst | 8 + test/README.md | 10 + 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test/assets/kaldi_test_mfcc_args.json | 3 - test/assets/kaldi_test_spectrogram_args.json | 109 +++++ test/common_utils.py | 80 ++-- ...t_functional.py => functional_cpu_test.py} | 44 +- test/functional_cuda_test.py | 16 + test/functional_impl.py | 31 ++ test/kaldi_compatibility_cpu_test.py | 11 +- test/kaldi_compatibility_cuda_test.py | 13 +- test/kaldi_compatibility_impl.py | 13 + test/test_backend.py | 42 ++ test/test_batch_consistency.py | 9 +- test/test_compliance_kaldi.py | 99 +--- test/test_dataloader.py | 8 +- test/test_datasets.py | 41 +- test/test_io.py | 59 +-- test/test_kaldi_io.py | 2 +- test/test_librosa_compatibility.py | 27 +- test/test_models.py | 51 ++- test/test_sox_compatibility.py | 61 +-- test/test_sox_effects.py | 47 +- test/test_transforms.py | 4 +- test/torchscript_consistency_cpu_test.py | 28 +- test/torchscript_consistency_cuda_test.py | 27 +- test/torchscript_consistency_impl.py | 1 + torchaudio/__init__.py | 430 ++---------------- torchaudio/_backend.py | 68 --- torchaudio/_internal/__init__.py | 0 torchaudio/_internal/misc_ops.py | 30 ++ torchaudio/_internal/module_utils.py | 56 +++ torchaudio/_sox_backend.py | 72 --- torchaudio/backend/__init__.py | 21 + torchaudio/backend/common.py | 143 ++++++ torchaudio/backend/no_backend.py | 35 ++ .../soundfile_backend.py} | 69 ++- torchaudio/backend/sox_backend.py | 275 +++++++++++ torchaudio/backend/utils.py | 74 +++ torchaudio/common_utils.py | 38 -- torchaudio/compliance/kaldi.py | 5 +- torchaudio/csrc/register.cpp | 18 + torchaudio/csrc/typedefs.cpp | 23 + torchaudio/csrc/typedefs.h | 23 + torchaudio/datasets/__init__.py | 2 + torchaudio/datasets/cmuarctic.py | 150 ++++++ torchaudio/extension/__init__.py | 7 + torchaudio/extension/extension.py | 49 ++ torchaudio/functional.py | 24 +- torchaudio/kaldi_io.py | 16 +- torchaudio/models/__init__.py | 1 + torchaudio/models/_wavernn.py | 105 +++++ torchaudio/sox_effects/__init__.py | 12 + torchaudio/{ => sox_effects}/sox_effects.py | 93 +++- 428 files 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test/{test_functional.py => functional_cpu_test.py} (90%) create mode 100644 test/functional_cuda_test.py create mode 100644 test/functional_impl.py create mode 100644 test/test_backend.py delete mode 100644 torchaudio/_backend.py create mode 100644 torchaudio/_internal/__init__.py create mode 100644 torchaudio/_internal/misc_ops.py create mode 100644 torchaudio/_internal/module_utils.py delete mode 100644 torchaudio/_sox_backend.py create mode 100644 torchaudio/backend/__init__.py create mode 100644 torchaudio/backend/common.py create mode 100644 torchaudio/backend/no_backend.py rename torchaudio/{_soundfile_backend.py => backend/soundfile_backend.py} (67%) create mode 100644 torchaudio/backend/sox_backend.py create mode 100644 torchaudio/backend/utils.py delete mode 100644 torchaudio/common_utils.py create mode 100644 torchaudio/csrc/register.cpp create mode 100644 torchaudio/csrc/typedefs.cpp create mode 100644 torchaudio/csrc/typedefs.h create mode 100644 torchaudio/datasets/cmuarctic.py create mode 100644 torchaudio/extension/__init__.py create mode 100644 torchaudio/extension/extension.py create mode 100644 torchaudio/models/_wavernn.py create mode 100644 torchaudio/sox_effects/__init__.py rename torchaudio/{ => sox_effects}/sox_effects.py (71%) diff --git a/docs/source/datasets.rst b/docs/source/datasets.rst index 8ba05dc670..2f844293f6 100644 --- a/docs/source/datasets.rst +++ b/docs/source/datasets.rst @@ -25,6 +25,14 @@ All the datasets have almost similar API. They all have two common arguments: .. currentmodule:: torchaudio.datasets +CMUARCTIC +~~~~~~~~~ + +.. autoclass:: CMUARCTIC + :members: __getitem__ + :special-members: + + COMMONVOICE ~~~~~~~~~~~ diff --git a/test/README.md b/test/README.md index a6686603af..35a926c120 100644 --- a/test/README.md +++ b/test/README.md @@ -44,6 +44,16 @@ The following test modules are defined for corresponding `torchaudio` module/fun ## Adding test +The following is the current practice of torchaudio test suite. + +1. Unless the tests are related to I/O, use synthetic data. [`common_utils`](./common_utils.py) has some data generator functions. +1. When you add a new test case, use `common_utils.TorchaudioTestCase` as base class unless you are writing tests that are common to CPU / CUDA. + - Set class memeber `dtype`, `device` and `backend` for the desired behavior. + - If you do not set `backend` value in your test suite, then I/O functions will be unassigned and attempt to load/save file will fail. + - For `backend` value, in addition to available backends, you can also provide the value "default" and backend will be picked automatically based on availability. +1. If you are writing tests that should pass on diffrent dtype/devices, write a common class inheriting `common_utils.TestBaseMixin`, then inherit `common_utils.PytorchTestCase` and define class attributes (`dtype` / `device` / `backend`) there. See [Torchscript consistency test implementation](./torchscript_consistency_impl.py) and test definitions for [CPU](./torchscript_consistency_cpu_test.py) and [CUDA](./torchscript_consistency_cuda_test.py) devices. +1. For numerically comparing Tensors, use `assertEqual` method from `common_utils.PytorchTestCase` class. This method has a better support for a wide variety of Tensor types. + When you add a new feature(functional/transform), consider the following 1. When you add a new feature, please make it Torchscript-able and batch-consistent unless it degrades the performance. Please add the tests to see if the new feature meet these requirements. diff --git a/test/assets/ARCTIC/cmu_us_aew_arctic/etc/txt.done.data b/test/assets/ARCTIC/cmu_us_aew_arctic/etc/txt.done.data new file mode 100644 index 0000000000..b6a7aa90ba --- /dev/null +++ b/test/assets/ARCTIC/cmu_us_aew_arctic/etc/txt.done.data @@ -0,0 +1 @@ +( arctic_a0024 "This is the test text." ) diff --git a/test/assets/ARCTIC/cmu_us_aew_arctic/wav/arctic_a0024.wav b/test/assets/ARCTIC/cmu_us_aew_arctic/wav/arctic_a0024.wav new file mode 100644 index 0000000000000000000000000000000000000000..b95ce95776fafd3e3cbaf670153f8d73167dfab3 GIT binary patch literal 441078 zcmWKXWmpqj6vqc_V;gLY9E@1dHM(JRcL_=eh=F2&9j{=EiHeA#0@96icXu}|=-9~7 zu-E5)x#xbkU+#0x`QP9DUl(f&3wk*i;9-2q{KC~+SR?=dVEH%9vH*bBo-6=X01{wl z?dpd9w{GoV;b`q@;fDA(xAVVqK|`CMszXpwRt5z6-||oUw~Gk<6BnU*CUeK6*Ivw8 z*ewZ7Lki>0D;STV`ps6vry6)dVVN$8`j(Y%V2NnhQo)=;R$#>R>XHSo;!`4z>YT<5 zrnLP%Y*NedQi;lRmlx4YsoliXpQT$g;X(NY%R#%ET84mNh=GL!>Z;|#ZDyK_T;O=x 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"num_ceps": 4, "cepstral_lifter": 3.5732, "vtln_high": 4583, "vtln_low": 3742, "vtln_warp": 1.0, "window_type": "povey", "dither": 0.0} {"blackman_coeff": 3.2416, "energy_floor": 1.0604, "frame_length": 0.875, "frame_shift": 0.5, "high_freq": 4730, "htk_compat": false, "low_freq": 968, "num_mel_bins": 4, "preemphasis_coefficient": 0.11, "raw_energy": true, "remove_dc_offset": true, "round_to_power_of_two": true, "snip_edges": false, "subtract_mean": true, "use_energy": true, "num_ceps": 3, "cepstral_lifter": 60.5751, "vtln_high": 3542, "vtln_low": 1943, "vtln_warp": 1.0, "window_type": "blackman", "dither": 0.0} {"blackman_coeff": 3.2698, "energy_floor": 3.0361, "frame_length": 1.0625, "frame_shift": 0.75, "high_freq": 4870, "htk_compat": true, "low_freq": 1281, "num_mel_bins": 7, "preemphasis_coefficient": 0.64, "raw_energy": false, "remove_dc_offset": true, "round_to_power_of_two": true, "snip_edges": false, "subtract_mean": true, "use_energy": true, "num_ceps": 3, "cepstral_lifter": 28.536, "vtln_high": 4401, "vtln_low": 3315, "vtln_warp": 1.0, "window_type": "blackman", "dither": 0.0} {"blackman_coeff": 3.3078, "energy_floor": 4.9217, "frame_length": 1.0, "frame_shift": 0.3125, "high_freq": 6758, "htk_compat": true, "low_freq": 760, "num_mel_bins": 5, "preemphasis_coefficient": 0.98, "raw_energy": true, "remove_dc_offset": true, "round_to_power_of_two": true, "snip_edges": true, "subtract_mean": false, "use_energy": true, "num_ceps": 3, "cepstral_lifter": 97.4694, "vtln_high": 6022, "vtln_low": 5650, "vtln_warp": 1.0, "window_type": "rectangular", "dither": 0.0} diff --git a/test/assets/kaldi_test_spectrogram_args.json b/test/assets/kaldi_test_spectrogram_args.json new file mode 100644 index 0000000000..c44245b6c0 --- /dev/null +++ b/test/assets/kaldi_test_spectrogram_args.json @@ -0,0 +1,109 @@ +{"blackman_coeff": 0.0016, "dither": 0, "energy_floor": 4.668, "frame_length": 0.625, "frame_shift": 0.25, "preemphasis_coefficient": 0.82, "raw_energy": false, 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0.5625, "preemphasis_coefficient": 0.27, "raw_energy": true, "remove_dc_offset": true, "round_to_power_of_two": true, "snip_edges": false, "subtract_mean": false, "window_type": "rectangular"} +{"blackman_coeff": 3.7759, "dither": 0, "energy_floor": 1.7002, "frame_length": 1.0625, "frame_shift": 0.6875, "preemphasis_coefficient": 0.42, "raw_energy": false, "remove_dc_offset": false, "round_to_power_of_two": true, "snip_edges": true, "subtract_mean": true, "window_type": "hamming"} +{"blackman_coeff": 3.7921, "dither": 0, "energy_floor": 3.4087, "frame_length": 0.25, "frame_shift": 1.0, "preemphasis_coefficient": 0.54, "raw_energy": true, "remove_dc_offset": true, "round_to_power_of_two": true, "snip_edges": false, "subtract_mean": false, "window_type": "blackman"} +{"blackman_coeff": 3.7954, "dither": 0, "energy_floor": 3.5651, "frame_length": 0.5, "frame_shift": 0.8125, "preemphasis_coefficient": 0.06, "raw_energy": false, "remove_dc_offset": true, "round_to_power_of_two": true, "snip_edges": false, "subtract_mean": true, "window_type": "blackman"} +{"blackman_coeff": 3.799, "dither": 0, "energy_floor": 3.0026, "frame_length": 0.625, "frame_shift": 1.0, "preemphasis_coefficient": 0.82, "raw_energy": false, "remove_dc_offset": true, "round_to_power_of_two": false, "snip_edges": true, "subtract_mean": true, "window_type": "hamming"} +{"blackman_coeff": 3.8659, "dither": 0, "energy_floor": 1.7487, "frame_length": 1.1875, "frame_shift": 0.375, "preemphasis_coefficient": 1.0, "raw_energy": true, "remove_dc_offset": true, "round_to_power_of_two": true, "snip_edges": true, "subtract_mean": false, "window_type": "hanning"} +{"blackman_coeff": 3.951, "dither": 0, "energy_floor": 0.3903, "frame_length": 1.125, "frame_shift": 1.0, "preemphasis_coefficient": 0.41, "raw_energy": true, "remove_dc_offset": false, "round_to_power_of_two": false, "snip_edges": true, "subtract_mean": true, "window_type": "rectangular"} +{"blackman_coeff": 4.0045, "dither": 0, "energy_floor": 3.061, "frame_length": 0.625, "frame_shift": 1.0625, "preemphasis_coefficient": 0.74, "raw_energy": false, "remove_dc_offset": false, "round_to_power_of_two": true, "snip_edges": false, "subtract_mean": true, "window_type": "hanning"} +{"blackman_coeff": 4.0187, "dither": 0, "energy_floor": 4.8148, "frame_length": 0.375, "frame_shift": 0.6875, "preemphasis_coefficient": 0.68, "raw_energy": true, "remove_dc_offset": true, "round_to_power_of_two": false, "snip_edges": false, "subtract_mean": false, "window_type": "hanning"} +{"blackman_coeff": 4.032, "dither": 0, "energy_floor": 2.2019, "frame_length": 1.125, "frame_shift": 0.25, "preemphasis_coefficient": 0.78, "raw_energy": true, "remove_dc_offset": true, "round_to_power_of_two": true, "snip_edges": true, "subtract_mean": true, "window_type": "rectangular"} +{"blackman_coeff": 4.0627, "dither": 0, "energy_floor": 4.1729, "frame_length": 0.625, "frame_shift": 1.125, "preemphasis_coefficient": 0.89, "raw_energy": false, "remove_dc_offset": false, "round_to_power_of_two": false, "snip_edges": false, "subtract_mean": false, "window_type": "povey"} +{"blackman_coeff": 4.0736, "dither": 0, "energy_floor": 0.9155, "frame_length": 1.0625, "frame_shift": 0.5625, "preemphasis_coefficient": 0.82, "raw_energy": false, "remove_dc_offset": true, "round_to_power_of_two": true, "snip_edges": true, "subtract_mean": true, "window_type": "hamming"} +{"blackman_coeff": 4.1131, "dither": 0, "energy_floor": 3.9204, "frame_length": 0.5, "frame_shift": 0.125, "preemphasis_coefficient": 0.39, "raw_energy": false, "remove_dc_offset": true, "round_to_power_of_two": true, "snip_edges": false, "subtract_mean": false, "window_type": "hanning"} +{"blackman_coeff": 4.1816, "dither": 0, "energy_floor": 1.665, "frame_length": 0.8125, "frame_shift": 0.375, "preemphasis_coefficient": 0.37, "raw_energy": true, "remove_dc_offset": false, "round_to_power_of_two": true, "snip_edges": true, "subtract_mean": false, "window_type": "rectangular"} +{"blackman_coeff": 4.1897, "dither": 0, "energy_floor": 1.2668, "frame_length": 0.1875, "frame_shift": 0.625, "preemphasis_coefficient": 0.74, "raw_energy": true, "remove_dc_offset": false, "round_to_power_of_two": true, "snip_edges": false, "subtract_mean": false, "window_type": "hamming"} +{"blackman_coeff": 4.2217, "dither": 0, "energy_floor": 3.6775, "frame_length": 0.3125, "frame_shift": 0.125, "preemphasis_coefficient": 0.01, "raw_energy": true, "remove_dc_offset": false, "round_to_power_of_two": true, "snip_edges": false, "subtract_mean": false, "window_type": "hamming"} +{"blackman_coeff": 4.2785, "dither": 0, "energy_floor": 0.7201, "frame_length": 0.8125, "frame_shift": 0.8125, "preemphasis_coefficient": 0.3, "raw_energy": false, "remove_dc_offset": false, "round_to_power_of_two": true, "snip_edges": false, "subtract_mean": true, "window_type": "hanning"} +{"blackman_coeff": 4.3304, "dither": 0, "energy_floor": 1.0538, "frame_length": 0.875, "frame_shift": 1.125, "preemphasis_coefficient": 0.92, "raw_energy": true, "remove_dc_offset": false, "round_to_power_of_two": true, "snip_edges": false, "subtract_mean": false, "window_type": "povey"} +{"blackman_coeff": 4.3942, "dither": 0, "energy_floor": 3.9813, "frame_length": 0.75, "frame_shift": 0.6875, "preemphasis_coefficient": 0.27, "raw_energy": false, "remove_dc_offset": true, "round_to_power_of_two": true, "snip_edges": true, "subtract_mean": true, "window_type": "blackman"} +{"blackman_coeff": 4.4432, "dither": 0, "energy_floor": 2.0441, "frame_length": 0.5, "frame_shift": 0.6875, "preemphasis_coefficient": 0.77, "raw_energy": false, "remove_dc_offset": false, "round_to_power_of_two": true, "snip_edges": true, "subtract_mean": false, "window_type": "hanning"} +{"blackman_coeff": 4.4459, "dither": 0, "energy_floor": 0.5135, "frame_length": 0.25, "frame_shift": 0.1875, "preemphasis_coefficient": 0.29, "raw_energy": false, "remove_dc_offset": false, "round_to_power_of_two": true, "snip_edges": false, "subtract_mean": true, "window_type": "hanning"} +{"blackman_coeff": 4.5486, "dither": 0, "energy_floor": 1.3248, "frame_length": 0.1875, "frame_shift": 1.125, "preemphasis_coefficient": 0.91, "raw_energy": false, "remove_dc_offset": false, "round_to_power_of_two": true, "snip_edges": false, "subtract_mean": true, "window_type": "hanning"} +{"blackman_coeff": 4.5535, "dither": 0, "energy_floor": 2.1772, "frame_length": 0.4375, "frame_shift": 0.875, "preemphasis_coefficient": 0.21, "raw_energy": true, "remove_dc_offset": false, "round_to_power_of_two": true, "snip_edges": true, "subtract_mean": true, "window_type": "hanning"} +{"blackman_coeff": 4.5835, "dither": 0, "energy_floor": 0.3781, "frame_length": 0.875, "frame_shift": 0.875, "preemphasis_coefficient": 0.04, "raw_energy": true, "remove_dc_offset": false, "round_to_power_of_two": true, "snip_edges": true, "subtract_mean": true, "window_type": "hamming"} +{"blackman_coeff": 4.6297, "dither": 0, "energy_floor": 2.49, "frame_length": 0.5, "frame_shift": 0.25, "preemphasis_coefficient": 0.03, "raw_energy": false, "remove_dc_offset": false, "round_to_power_of_two": true, "snip_edges": true, "subtract_mean": false, "window_type": "rectangular"} +{"blackman_coeff": 4.6749, "dither": 0, "energy_floor": 4.8853, "frame_length": 0.25, "frame_shift": 0.25, "preemphasis_coefficient": 0.48, "raw_energy": false, "remove_dc_offset": true, "round_to_power_of_two": false, "snip_edges": false, "subtract_mean": false, "window_type": "povey"} +{"blackman_coeff": 4.6971, "dither": 0, "energy_floor": 1.3632, "frame_length": 0.875, "frame_shift": 0.9375, "preemphasis_coefficient": 0.44, "raw_energy": false, "remove_dc_offset": true, "round_to_power_of_two": false, "snip_edges": true, "subtract_mean": false, "window_type": "blackman"} diff --git a/test/common_utils.py b/test/common_utils.py index 00d10a6df1..fe045d9db8 100644 --- a/test/common_utils.py +++ b/test/common_utils.py @@ -1,16 +1,15 @@ import os import tempfile import unittest -from typing import Type, Iterable, Union -from contextlib import contextmanager +from typing import Union from shutil import copytree import torch -from torch.testing._internal.common_utils import TestCase +from torch.testing._internal.common_utils import TestCase as PytorchTestCase import torchaudio _TEST_DIR_PATH = os.path.dirname(os.path.realpath(__file__)) -BACKENDS = torchaudio._backend._audio_backends +BACKENDS = torchaudio.list_audio_backends() def get_asset_path(*paths): @@ -55,24 +54,14 @@ def random_float_tensor(seed, size, a=22695477, c=1, m=2 ** 32): return torch.tensor(arr).float().view(size) / m -@contextmanager -def AudioBackendScope(new_backend): - previous_backend = torchaudio.get_audio_backend() - try: - torchaudio.set_audio_backend(new_backend) - yield - finally: - torchaudio.set_audio_backend(previous_backend) - - def filter_backends_with_mp3(backends): # Filter out backends that do not support mp3 test_filepath = get_asset_path('steam-train-whistle-daniel_simon.mp3') def supports_mp3(backend): + torchaudio.set_audio_backend(backend) try: - with AudioBackendScope(backend): - torchaudio.load(test_filepath) + torchaudio.load(test_filepath) return True except (RuntimeError, ImportError): return False @@ -83,49 +72,38 @@ def supports_mp3(backend): BACKENDS_MP3 = filter_backends_with_mp3(BACKENDS) -class TestBaseMixin: - dtype = None - device = None - - -_SKIP_IF_NO_CUDA = unittest.skipIf(not torch.cuda.is_available(), reason='CUDA not available') +def set_audio_backend(backend): + """Allow additional backend value, 'default'""" + if backend == 'default': + if 'sox' in BACKENDS: + be = 'sox' + elif 'soundfile' in BACKENDS: + be = 'soundfile' + else: + raise unittest.SkipTest('No default backend available') + else: + be = backend + torchaudio.set_audio_backend(be) -def define_test_suite(testbase: Type[TestBaseMixin], dtype: str, device: str): - if dtype not in ['float32', 'float64']: - raise NotImplementedError(f'Unexpected dtype: {dtype}') - if device not in ['cpu', 'cuda']: - raise NotImplementedError(f'Unexpected device: {device}') - name = f'Test{testbase.__name__}_{device.upper()}_{dtype.capitalize()}' - attrs = {'dtype': getattr(torch, dtype), 'device': torch.device(device)} - testsuite = type(name, (testbase, TestCase), attrs) +class TestBaseMixin: + """Mixin to provide consistent way to define device/dtype/backend aware TestCase""" + dtype = None + device = None + backend = None - if device == 'cuda': - testsuite = _SKIP_IF_NO_CUDA(testsuite) - return testsuite + def setUp(self): + super().setUp() + set_audio_backend(self.backend) -def define_test_suites( - scope: dict, - testbases: Iterable[Type[TestBaseMixin]], - dtypes: Iterable[str] = ('float32', 'float64'), - devices: Iterable[str] = ('cpu', 'cuda'), -): - for suite in testbases: - for device in devices: - for dtype in dtypes: - t = define_test_suite(suite, dtype, device) - scope[t.__name__] = t +class TorchaudioTestCase(TestBaseMixin, PytorchTestCase): + pass -def common_test_class_parameters( - dtypes: Iterable[str] = ("float32", "float64"), - devices: Iterable[str] = ("cpu", "cuda"), -): - for device in devices: - for dtype in dtypes: - yield {"device": torch.device(device), "dtype": getattr(torch, dtype)} +skipIfNoSoxBackend = unittest.skipIf('sox' not in BACKENDS, 'Sox backend not available') +skipIfNoCuda = unittest.skipIf(not torch.cuda.is_available(), reason='CUDA not available') def get_whitenoise( diff --git a/test/test_functional.py b/test/functional_cpu_test.py similarity index 90% rename from test/test_functional.py rename to test/functional_cpu_test.py index ac90b7bde5..470d6ab770 100644 --- a/test/test_functional.py +++ b/test/functional_cpu_test.py @@ -7,38 +7,20 @@ import pytest from . import common_utils +from .functional_impl import Lfilter -class Lfilter(common_utils.TestBaseMixin): - def test_simple(self): - """ - Create a very basic signal, - Then make a simple 4th order delay - The output should be same as the input but shifted - """ +class TestLFilterFloat32(Lfilter, common_utils.PytorchTestCase): + dtype = torch.float32 + device = torch.device('cpu') - torch.random.manual_seed(42) - waveform = torch.rand(2, 44100 * 1, dtype=self.dtype, device=self.device) - b_coeffs = torch.tensor([0, 0, 0, 1], dtype=self.dtype, device=self.device) - a_coeffs = torch.tensor([1, 0, 0, 0], dtype=self.dtype, device=self.device) - output_waveform = F.lfilter(waveform, a_coeffs, b_coeffs) - self.assertEqual(output_waveform[:, 3:], waveform[:, 0:-3], atol=1e-5, rtol=1e-5) +class TestLFilterFloat64(Lfilter, common_utils.PytorchTestCase): + dtype = torch.float64 + device = torch.device('cpu') - def test_clamp(self): - input_signal = torch.ones(1, 44100 * 1, dtype=self.dtype, device=self.device) - b_coeffs = torch.tensor([1, 0], dtype=self.dtype, device=self.device) - a_coeffs = torch.tensor([1, -0.95], dtype=self.dtype, device=self.device) - output_signal = F.lfilter(input_signal, a_coeffs, b_coeffs, clamp=True) - assert output_signal.max() <= 1 - output_signal = F.lfilter(input_signal, a_coeffs, b_coeffs, clamp=False) - assert output_signal.max() > 1 - -common_utils.define_test_suites(globals(), [Lfilter]) - - -class TestComputeDeltas(unittest.TestCase): +class TestComputeDeltas(common_utils.TorchaudioTestCase): """Test suite for correctness of compute_deltas""" def test_one_channel(self): specgram = torch.tensor([[[1.0, 2.0, 3.0, 4.0]]]) @@ -75,7 +57,7 @@ def _test_istft_is_inverse_of_stft(kwargs): _compare_estimate(sound, estimate) -class TestIstft(unittest.TestCase): +class TestIstft(common_utils.TorchaudioTestCase): """Test suite for correctness of istft with various input""" number_of_trials = 100 @@ -291,7 +273,9 @@ def test_linearity_of_istft4(self): self._test_linearity_of_istft(data_size, kwargs4, atol=1e-5, rtol=1e-8) -class TestDetectPitchFrequency(unittest.TestCase): +class TestDetectPitchFrequency(common_utils.TorchaudioTestCase): + backend = 'default' + def test_pitch(self): test_filepath_100 = common_utils.get_asset_path("100Hz_44100Hz_16bit_05sec.wav") test_filepath_440 = common_utils.get_asset_path("440Hz_44100Hz_16bit_05sec.wav") @@ -312,7 +296,7 @@ def test_pitch(self): self.assertFalse(s) -class TestDB_to_amplitude(unittest.TestCase): +class TestDB_to_amplitude(common_utils.TorchaudioTestCase): def test_DB_to_amplitude(self): # Make some noise x = torch.rand(1000) @@ -381,7 +365,7 @@ def test_mask_along_axis(specgram, mask_param, mask_value, axis): masked_columns = (mask_specgram == mask_value).sum(other_axis) num_masked_columns = (masked_columns == mask_specgram.size(other_axis)).sum() - num_masked_columns /= mask_specgram.size(0) + num_masked_columns //= mask_specgram.size(0) assert mask_specgram.size() == specgram.size() assert num_masked_columns < mask_param diff --git a/test/functional_cuda_test.py b/test/functional_cuda_test.py new file mode 100644 index 0000000000..9602dcf071 --- /dev/null +++ b/test/functional_cuda_test.py @@ -0,0 +1,16 @@ +import torch + +from . import common_utils +from .functional_impl import Lfilter + + +@common_utils.skipIfNoCuda +class TestLFilterFloat32(Lfilter, common_utils.PytorchTestCase): + dtype = torch.float32 + device = torch.device('cuda') + + +@common_utils.skipIfNoCuda +class TestLFilterFloat64(Lfilter, common_utils.PytorchTestCase): + dtype = torch.float64 + device = torch.device('cuda') diff --git a/test/functional_impl.py b/test/functional_impl.py new file mode 100644 index 0000000000..fb5e7dc7ac --- /dev/null +++ b/test/functional_impl.py @@ -0,0 +1,31 @@ +"""Test defintion common to CPU and CUDA""" +import torch +import torchaudio.functional as F + +from . import common_utils + + +class Lfilter(common_utils.TestBaseMixin): + def test_simple(self): + """ + Create a very basic signal, + Then make a simple 4th order delay + The output should be same as the input but shifted + """ + + torch.random.manual_seed(42) + waveform = torch.rand(2, 44100 * 1, dtype=self.dtype, device=self.device) + b_coeffs = torch.tensor([0, 0, 0, 1], dtype=self.dtype, device=self.device) + a_coeffs = torch.tensor([1, 0, 0, 0], dtype=self.dtype, device=self.device) + output_waveform = F.lfilter(waveform, a_coeffs, b_coeffs) + + self.assertEqual(output_waveform[:, 3:], waveform[:, 0:-3], atol=1e-5, rtol=1e-5) + + def test_clamp(self): + input_signal = torch.ones(1, 44100 * 1, dtype=self.dtype, device=self.device) + b_coeffs = torch.tensor([1, 0], dtype=self.dtype, device=self.device) + a_coeffs = torch.tensor([1, -0.95], dtype=self.dtype, device=self.device) + output_signal = F.lfilter(input_signal, a_coeffs, b_coeffs, clamp=True) + assert output_signal.max() <= 1 + output_signal = F.lfilter(input_signal, a_coeffs, b_coeffs, clamp=False) + assert output_signal.max() > 1 diff --git a/test/kaldi_compatibility_cpu_test.py b/test/kaldi_compatibility_cpu_test.py index cd85074ac0..3357123296 100644 --- a/test/kaldi_compatibility_cpu_test.py +++ b/test/kaldi_compatibility_cpu_test.py @@ -1,5 +1,14 @@ +import torch + from . import common_utils from .kaldi_compatibility_impl import Kaldi -common_utils.define_test_suites(globals(), [Kaldi], devices=['cpu']) +class TestKaldiFloat32(Kaldi, common_utils.PytorchTestCase): + dtype = torch.float32 + device = torch.device('cpu') + + +class TestKaldiFloat64(Kaldi, common_utils.PytorchTestCase): + dtype = torch.float64 + device = torch.device('cpu') diff --git a/test/kaldi_compatibility_cuda_test.py b/test/kaldi_compatibility_cuda_test.py index acaa2408f5..e23dbf3f40 100644 --- a/test/kaldi_compatibility_cuda_test.py +++ b/test/kaldi_compatibility_cuda_test.py @@ -1,5 +1,16 @@ +import torch + from . import common_utils from .kaldi_compatibility_impl import Kaldi -common_utils.define_test_suites(globals(), [Kaldi], devices=['cuda']) +@common_utils.skipIfNoCuda +class TestKaldiFloat32(Kaldi, common_utils.PytorchTestCase): + dtype = torch.float32 + device = torch.device('cuda') + + +@common_utils.skipIfNoCuda +class TestKaldiFloat64(Kaldi, common_utils.PytorchTestCase): + dtype = torch.float64 + device = torch.device('cuda') diff --git a/test/kaldi_compatibility_impl.py b/test/kaldi_compatibility_impl.py index 2d3313d007..ec58a3fa44 100644 --- a/test/kaldi_compatibility_impl.py +++ b/test/kaldi_compatibility_impl.py @@ -55,6 +55,8 @@ def _load_params(path): class Kaldi(common_utils.TestBaseMixin): + backend = 'sox' + def assert_equal(self, output, *, expected, rtol=None, atol=None): expected = expected.to(dtype=self.dtype, device=self.device) self.assertEqual(output, expected, rtol=rtol, atol=atol) @@ -86,6 +88,17 @@ def test_fbank(self, kwargs): kaldi_result = _run_kaldi(command, 'scp', wave_file) self.assert_equal(result, expected=kaldi_result, rtol=1e-4, atol=1e-8) + @parameterized.expand(_load_params(common_utils.get_asset_path('kaldi_test_spectrogram_args.json'))) + @unittest.skipIf(_not_available('compute-spectrogram-feats'), '`compute-spectrogram-feats` not available') + def test_spectrogram(self, kwargs): + """spectrogram should be numerically compatible with compute-spectrogram-feats""" + wave_file = common_utils.get_asset_path('kaldi_file.wav') + waveform = torchaudio.load_wav(wave_file)[0].to(dtype=self.dtype, device=self.device) + result = torchaudio.compliance.kaldi.spectrogram(waveform, **kwargs) + command = ['compute-spectrogram-feats'] + _convert_args(**kwargs) + ['scp:-', 'ark:-'] + kaldi_result = _run_kaldi(command, 'scp', wave_file) + self.assert_equal(result, expected=kaldi_result, rtol=1e-4, atol=1e-8) + @parameterized.expand(_load_params(common_utils.get_asset_path('kaldi_test_mfcc_args.json'))) @unittest.skipIf(_not_available('compute-mfcc-feats'), '`compute-mfcc-feats` not available') def test_mfcc(self, kwargs): diff --git a/test/test_backend.py b/test/test_backend.py new file mode 100644 index 0000000000..1e8f9e4fd6 --- /dev/null +++ b/test/test_backend.py @@ -0,0 +1,42 @@ +import unittest + +import torchaudio +from torchaudio._internal.module_utils import is_module_available + +from . import common_utils + + +class BackendSwitchMixin: + """Test set/get_audio_backend works""" + backend = None + backend_module = None + + def test_switch(self): + torchaudio.set_audio_backend(self.backend) + if self.backend is None: + assert torchaudio.get_audio_backend() is None + else: + assert torchaudio.get_audio_backend() == self.backend + assert torchaudio.load == self.backend_module.load + assert torchaudio.load_wav == self.backend_module.load_wav + assert torchaudio.save == self.backend_module.save + assert torchaudio.info == self.backend_module.info + + +class TestBackendSwitch_NoBackend(BackendSwitchMixin, common_utils.TorchaudioTestCase): + backend = None + backend_module = torchaudio.backend.no_backend + + +@unittest.skipIf( + not is_module_available('torchaudio._torchaudio'), + 'torchaudio C++ extension not available') +class TestBackendSwitch_SoX(BackendSwitchMixin, common_utils.TorchaudioTestCase): + backend = 'sox' + backend_module = torchaudio.backend.sox_backend + + +@unittest.skipIf(not is_module_available('soundfile'), '"soundfile" not available') +class TestBackendSwitch_soundfile(BackendSwitchMixin, common_utils.TorchaudioTestCase): + backend = 'soundfile' + backend_module = torchaudio.backend.soundfile_backend diff --git a/test/test_batch_consistency.py b/test/test_batch_consistency.py index 103d83bb84..2a88598f88 100644 --- a/test/test_batch_consistency.py +++ b/test/test_batch_consistency.py @@ -2,14 +2,14 @@ import unittest import torch -from torch.testing._internal.common_utils import TestCase import torchaudio import torchaudio.functional as F from . import common_utils -class TestFunctional(TestCase): +class TestFunctional(common_utils.TorchaudioTestCase): + backend = 'default' """Test functions defined in `functional` module""" def assert_batch_consistency( self, functional, tensor, *args, batch_size=1, atol=1e-8, rtol=1e-5, seed=42, **kwargs): @@ -98,12 +98,15 @@ def test_sliding_window_cmn(self): self.assert_batch_consistencies(F.sliding_window_cmn, waveform, center=False, norm_vars=False) def test_vad(self): + common_utils.set_audio_backend('default') filepath = common_utils.get_asset_path("vad-go-mono-32000.wav") waveform, sample_rate = torchaudio.load(filepath) self.assert_batch_consistencies(F.vad, waveform, sample_rate=sample_rate) -class TestTransforms(TestCase): +class TestTransforms(common_utils.TorchaudioTestCase): + backend = 'default' + """Test suite for classes defined in `transforms` module""" def test_batch_AmplitudeToDB(self): spec = torch.rand((6, 201)) diff --git a/test/test_compliance_kaldi.py b/test/test_compliance_kaldi.py index 2a255a5a4b..f9e097e09c 100644 --- a/test/test_compliance_kaldi.py +++ b/test/test_compliance_kaldi.py @@ -1,13 +1,13 @@ -import math import os +import math +import unittest + import torch import torchaudio import torchaudio.compliance.kaldi as kaldi -import unittest from . import common_utils from .compliance import utils as compliance_utils -from .common_utils import AudioBackendScope, BACKENDS def extract_window(window, wave, f, frame_length, frame_shift, snip_edges): @@ -46,7 +46,10 @@ def first_sample_of_frame(frame, window_size, window_shift, snip_edges): window[f, s] = wave[s_in_wave] -class Test_Kaldi(unittest.TestCase): +@common_utils.skipIfNoSoxBackend +class Test_Kaldi(common_utils.TorchaudioTestCase): + backend = 'sox' + test_filepath = common_utils.get_asset_path('kaldi_file.wav') test_8000_filepath = common_utils.get_asset_path('kaldi_file_8000.wav') kaldi_output_dir = common_utils.get_asset_path('kaldi') @@ -158,98 +161,10 @@ def _compliance_test_helper(self, sound_filepath, filepath_key, expected_num_fil self._print_diagnostic(output, kaldi_output) torch.testing.assert_allclose(output, kaldi_output, atol=atol, rtol=rtol) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") - def test_spectrogram(self): - def get_output_fn(sound, args): - output = kaldi.spectrogram( - sound, - blackman_coeff=args[1], - dither=args[2], - energy_floor=args[3], - frame_length=args[4], - frame_shift=args[5], - preemphasis_coefficient=args[6], - raw_energy=args[7], - remove_dc_offset=args[8], - round_to_power_of_two=args[9], - snip_edges=args[10], - subtract_mean=args[11], - window_type=args[12]) - return output - - self._compliance_test_helper(self.test_filepath, 'spec', 131, 13, get_output_fn, atol=1e-3, rtol=0) - - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") - def test_fbank(self): - def get_output_fn(sound, args): - output = kaldi.fbank( - sound, - blackman_coeff=args[1], - dither=0.0, - energy_floor=args[2], - frame_length=args[3], - frame_shift=args[4], - high_freq=args[5], - htk_compat=args[6], - low_freq=args[7], - num_mel_bins=args[8], - preemphasis_coefficient=args[9], - raw_energy=args[10], - remove_dc_offset=args[11], - round_to_power_of_two=args[12], - snip_edges=args[13], - subtract_mean=args[14], - use_energy=args[15], - use_log_fbank=args[16], - use_power=args[17], - vtln_high=args[18], - vtln_low=args[19], - vtln_warp=args[20], - window_type=args[21]) - return output - - self._compliance_test_helper(self.test_filepath, 'fbank', 97, 22, get_output_fn, atol=1e-3, rtol=1e-1) - - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") - def test_mfcc(self): - def get_output_fn(sound, args): - output = kaldi.mfcc( - sound, - blackman_coeff=args[1], - dither=0.0, - energy_floor=args[2], - frame_length=args[3], - frame_shift=args[4], - high_freq=args[5], - htk_compat=args[6], - low_freq=args[7], - num_mel_bins=args[8], - preemphasis_coefficient=args[9], - raw_energy=args[10], - remove_dc_offset=args[11], - round_to_power_of_two=args[12], - snip_edges=args[13], - subtract_mean=args[14], - use_energy=args[15], - num_ceps=args[16], - cepstral_lifter=args[17], - vtln_high=args[18], - vtln_low=args[19], - vtln_warp=args[20], - window_type=args[21]) - return output - - self._compliance_test_helper(self.test_filepath, 'mfcc', 145, 22, get_output_fn, atol=1e-3) - def test_mfcc_empty(self): # Passing in an empty tensor should result in an error self.assertRaises(AssertionError, kaldi.mfcc, torch.empty(0)) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_resample_waveform(self): def get_output_fn(sound, args): output = kaldi.resample_waveform(sound, args[1], args[2]) diff --git a/test/test_dataloader.py b/test/test_dataloader.py index cfcc25e7c4..1bf73ebb64 100644 --- a/test/test_dataloader.py +++ b/test/test_dataloader.py @@ -4,7 +4,6 @@ from torch.utils.data import Dataset, DataLoader from . import common_utils -from .common_utils import AudioBackendScope, BACKENDS class TORCHAUDIODS(Dataset): @@ -28,9 +27,10 @@ def __len__(self): return len(self.data) -class Test_DataLoader(unittest.TestCase): - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") +class Test_DataLoader(common_utils.TorchaudioTestCase): + backend = 'sox' + + @common_utils.skipIfNoSoxBackend def test_1(self): expected_size = (2, 1, 16000) ds = TORCHAUDIODS() diff --git a/test/test_datasets.py b/test/test_datasets.py index ab1e74e888..3ac7fae8ac 100644 --- a/test/test_datasets.py +++ b/test/test_datasets.py @@ -8,11 +8,13 @@ from torchaudio.datasets.yesno import YESNO from torchaudio.datasets.ljspeech import LJSPEECH from torchaudio.datasets.gtzan import GTZAN +from torchaudio.datasets.cmuarctic import CMUARCTIC from . import common_utils -class TestDatasets(unittest.TestCase): +class TestDatasets(common_utils.TorchaudioTestCase): + backend = 'default' path = common_utils.get_asset_path() def test_yesno(self): @@ -27,12 +29,32 @@ def test_librispeech(self): data = LIBRISPEECH(self.path, "dev-clean") data[0] - @unittest.skipIf("sox" not in common_utils.BACKENDS, "sox not available") + def test_ljspeech(self): + data = LJSPEECH(self.path) + data[0] + + def test_speechcommands(self): + data = SPEECHCOMMANDS(self.path) + data[0] + + def test_gtzan(self): + data = GTZAN(self.path) + data[0] + + def test_cmuarctic(self): + data = CMUARCTIC(self.path) + data[0] + + +@common_utils.skipIfNoSoxBackend +class TestCommonVoice(common_utils.TorchaudioTestCase): + backend = 'sox' + path = common_utils.get_asset_path() + def test_commonvoice(self): data = COMMONVOICE(self.path, url="tatar") data[0] - @unittest.skipIf("sox" not in common_utils.BACKENDS, "sox not available") def test_commonvoice_diskcache(self): data = COMMONVOICE(self.path, url="tatar") data = diskcache_iterator(data) @@ -41,25 +63,12 @@ def test_commonvoice_diskcache(self): # Load data[0] - @unittest.skipIf("sox" not in common_utils.BACKENDS, "sox not available") def test_commonvoice_bg(self): data = COMMONVOICE(self.path, url="tatar") data = bg_iterator(data, 5) for _ in data: pass - def test_ljspeech(self): - data = LJSPEECH(self.path) - data[0] - - def test_speechcommands(self): - data = SPEECHCOMMANDS(self.path) - data[0] - - def test_gtzan(self): - data = GTZAN(self.path) - data[0] - if __name__ == "__main__": unittest.main() diff --git a/test/test_io.py b/test/test_io.py index 0295cb785e..f58f66ed11 100644 --- a/test/test_io.py +++ b/test/test_io.py @@ -1,9 +1,11 @@ +import os +import math import unittest + import torch import torchaudio -import math -import os -from .common_utils import AudioBackendScope, BACKENDS, BACKENDS_MP3, create_temp_assets_dir + +from .common_utils import BACKENDS, BACKENDS_MP3, create_temp_assets_dir class Test_LoadSave(unittest.TestCase): @@ -16,13 +18,13 @@ class Test_LoadSave(unittest.TestCase): def test_1_save(self): for backend in BACKENDS_MP3: with self.subTest(): - with AudioBackendScope(backend): - self._test_1_save(self.test_filepath, False) + torchaudio.set_audio_backend(backend) + self._test_1_save(self.test_filepath, False) for backend in BACKENDS: with self.subTest(): - with AudioBackendScope(backend): - self._test_1_save(self.test_filepath_wav, True) + torchaudio.set_audio_backend(backend) + self._test_1_save(self.test_filepath_wav, True) def _test_1_save(self, test_filepath, normalization): # load signal @@ -67,8 +69,8 @@ def _test_1_save(self, test_filepath, normalization): def test_1_save_sine(self): for backend in BACKENDS: with self.subTest(): - with AudioBackendScope(backend): - self._test_1_save_sine() + torchaudio.set_audio_backend(backend) + self._test_1_save_sine() def _test_1_save_sine(self): @@ -100,13 +102,13 @@ def _test_1_save_sine(self): def test_2_load(self): for backend in BACKENDS_MP3: with self.subTest(): - with AudioBackendScope(backend): - self._test_2_load(self.test_filepath, 278756) + torchaudio.set_audio_backend(backend) + self._test_2_load(self.test_filepath, 278756) for backend in BACKENDS: with self.subTest(): - with AudioBackendScope(backend): - self._test_2_load(self.test_filepath_wav, 276858) + torchaudio.set_audio_backend(backend) + self._test_2_load(self.test_filepath_wav, 276858) def _test_2_load(self, test_filepath, length): # check normal loading @@ -141,8 +143,8 @@ def _test_2_load(self, test_filepath, length): def test_2_load_nonormalization(self): for backend in BACKENDS_MP3: with self.subTest(): - with AudioBackendScope(backend): - self._test_2_load_nonormalization(self.test_filepath, 278756) + torchaudio.set_audio_backend(backend) + self._test_2_load_nonormalization(self.test_filepath, 278756) def _test_2_load_nonormalization(self, test_filepath, length): @@ -158,8 +160,8 @@ def _test_2_load_nonormalization(self, test_filepath, length): def test_3_load_and_save_is_identity(self): for backend in BACKENDS: with self.subTest(): - with AudioBackendScope(backend): - self._test_3_load_and_save_is_identity() + torchaudio.set_audio_backend(backend) + self._test_3_load_and_save_is_identity() def _test_3_load_and_save_is_identity(self): input_path = os.path.join(self.test_dirpath, 'assets', 'sinewave.wav') @@ -179,16 +181,15 @@ def test_3_load_and_save_is_identity_across_backend(self): self._test_3_load_and_save_is_identity_across_backend("soundfile", "sox") def _test_3_load_and_save_is_identity_across_backend(self, backend1, backend2): - with AudioBackendScope(backend1): - - input_path = os.path.join(self.test_dirpath, 'assets', 'sinewave.wav') - tensor1, sample_rate1 = torchaudio.load(input_path) + torchaudio.set_audio_backend(backend1) + input_path = os.path.join(self.test_dirpath, 'assets', 'sinewave.wav') + tensor1, sample_rate1 = torchaudio.load(input_path) - output_path = os.path.join(self.test_dirpath, 'test.wav') - torchaudio.save(output_path, tensor1, sample_rate1) + output_path = os.path.join(self.test_dirpath, 'test.wav') + torchaudio.save(output_path, tensor1, sample_rate1) - with AudioBackendScope(backend2): - tensor2, sample_rate2 = torchaudio.load(output_path) + torchaudio.set_audio_backend(backend2) + tensor2, sample_rate2 = torchaudio.load(output_path) self.assertTrue(tensor1.allclose(tensor2)) self.assertEqual(sample_rate1, sample_rate2) @@ -197,8 +198,8 @@ def _test_3_load_and_save_is_identity_across_backend(self, backend1, backend2): def test_4_load_partial(self): for backend in BACKENDS_MP3: with self.subTest(): - with AudioBackendScope(backend): - self._test_4_load_partial() + torchaudio.set_audio_backend(backend) + self._test_4_load_partial() def _test_4_load_partial(self): num_frames = 101 @@ -239,8 +240,8 @@ def _test_4_load_partial(self): def test_5_get_info(self): for backend in BACKENDS: with self.subTest(): - with AudioBackendScope(backend): - self._test_5_get_info() + torchaudio.set_audio_backend(backend) + self._test_5_get_info() def _test_5_get_info(self): input_path = os.path.join(self.test_dirpath, 'assets', 'sinewave.wav') diff --git a/test/test_kaldi_io.py b/test/test_kaldi_io.py index 942b11de35..4bd0fd56a4 100644 --- a/test/test_kaldi_io.py +++ b/test/test_kaldi_io.py @@ -6,7 +6,7 @@ from . import common_utils -class Test_KaldiIO(unittest.TestCase): +class Test_KaldiIO(common_utils.TorchaudioTestCase): data1 = [[1, 2, 3], [11, 12, 13], [21, 22, 23]] data2 = [[31, 32, 33], [41, 42, 43], [51, 52, 53]] diff --git a/test/test_librosa_compatibility.py b/test/test_librosa_compatibility.py index 665341d945..62e9d3ca88 100644 --- a/test/test_librosa_compatibility.py +++ b/test/test_librosa_compatibility.py @@ -4,12 +4,13 @@ from distutils.version import StrictVersion import torch -from torch.testing._internal.common_utils import TestCase import torchaudio import torchaudio.functional as F -from torchaudio.common_utils import IMPORT_LIBROSA +from torchaudio._internal.module_utils import is_module_available -if IMPORT_LIBROSA: +LIBROSA_AVAILABLE = is_module_available('librosa') + +if LIBROSA_AVAILABLE: import numpy as np import librosa import scipy @@ -19,8 +20,8 @@ from . import common_utils -@unittest.skipIf(not IMPORT_LIBROSA, "Librosa not available") -class TestFunctional(TestCase): +@unittest.skipIf(not LIBROSA_AVAILABLE, "Librosa not available") +class TestFunctional(common_utils.TorchaudioTestCase): """Test suite for functions in `functional` module.""" def test_griffinlim(self): # NOTE: This test is flaky without a fixed random seed @@ -115,12 +116,8 @@ def test_amplitude_to_DB(self): ]) @pytest.mark.parametrize('rate', [0.5, 1.01, 1.3]) @pytest.mark.parametrize('hop_length', [256]) +@unittest.skipIf(not LIBROSA_AVAILABLE, "Librosa not available") def test_phase_vocoder(complex_specgrams, rate, hop_length): - - # Using a decorator here causes parametrize to fail on Python 2 - if not IMPORT_LIBROSA: - raise unittest.SkipTest('Librosa is not available') - # Due to cummulative sum, numerical error in using torch.float32 will # result in bottom right values of the stretched sectrogram to not # match with librosa. @@ -158,10 +155,11 @@ def _load_audio_asset(*asset_paths, **kwargs): return sound, sample_rate -@unittest.skipIf(not IMPORT_LIBROSA, "Librosa not available") -class TestTransforms(TestCase): +@unittest.skipIf(not LIBROSA_AVAILABLE, "Librosa not available") +class TestTransforms(common_utils.TorchaudioTestCase): """Test suite for functions in `transforms` module.""" def assert_compatibilities(self, n_fft, hop_length, power, n_mels, n_mfcc, sample_rate): + common_utils.set_audio_backend('default') sound, sample_rate = _load_audio_asset('sinewave.wav') sound_librosa = sound.cpu().numpy().squeeze() # (64000) @@ -271,8 +269,7 @@ def test_basics4(self): } self.assert_compatibilities(**kwargs) - @unittest.skipIf("sox" not in common_utils.BACKENDS, "sox not available") - @common_utils.AudioBackendScope("sox") + @unittest.skipIf(not common_utils.BACKENDS_MP3, 'no backend to read mp3') def test_MelScale(self): """MelScale transform is comparable to that of librosa""" n_fft = 2048 @@ -280,6 +277,7 @@ def test_MelScale(self): hop_length = n_fft // 4 # Prepare spectrogram input. We use torchaudio to compute one. + common_utils.set_audio_backend('default') sound, sample_rate = _load_audio_asset('whitenoise_1min.mp3') sound = sound.mean(dim=0, keepdim=True) spec_ta = F.spectrogram( @@ -302,6 +300,7 @@ def test_InverseMelScale(self): hop_length = n_fft // 4 # Prepare mel spectrogram input. We use torchaudio to compute one. + common_utils.set_audio_backend('default') sound, sample_rate = _load_audio_asset( 'steam-train-whistle-daniel_simon.wav', offset=2**10, num_frames=2**14) sound = sound.mean(dim=0, keepdim=True) diff --git a/test/test_models.py b/test/test_models.py index fe8df9a167..7bd3f3819d 100644 --- a/test/test_models.py +++ b/test/test_models.py @@ -1,30 +1,51 @@ -import pytest - import torch -from torchaudio.models import Wav2Letter +from torchaudio.models import Wav2Letter, _MelResNet class TestWav2Letter: - @pytest.mark.parametrize('batch_size', [2]) - @pytest.mark.parametrize('num_features', [1]) - @pytest.mark.parametrize('num_classes', [40]) - @pytest.mark.parametrize('input_length', [320]) - def test_waveform(self, batch_size, num_features, num_classes, input_length): - model = Wav2Letter() + + def test_waveform(self): + batch_size = 2 + num_features = 1 + num_classes = 40 + input_length = 320 + + model = Wav2Letter(num_classes=num_classes, num_features=num_features) x = torch.rand(batch_size, num_features, input_length) out = model(x) assert out.size() == (batch_size, num_classes, 2) - @pytest.mark.parametrize('batch_size', [2]) - @pytest.mark.parametrize('num_features', [13]) - @pytest.mark.parametrize('num_classes', [40]) - @pytest.mark.parametrize('input_length', [2]) - def test_mfcc(self, batch_size, num_features, num_classes, input_length): - model = Wav2Letter(input_type="mfcc", num_features=13) + def test_mfcc(self): + batch_size = 2 + num_features = 13 + num_classes = 40 + input_length = 2 + + model = Wav2Letter(num_classes=num_classes, input_type="mfcc", num_features=num_features) x = torch.rand(batch_size, num_features, input_length) out = model(x) assert out.size() == (batch_size, num_classes, 2) + + +class TestMelResNet: + + def test_waveform(self): + + batch_size = 2 + num_features = 200 + input_dims = 100 + output_dims = 128 + res_blocks = 10 + hidden_dims = 128 + pad = 2 + + model = _MelResNet(res_blocks, input_dims, hidden_dims, output_dims, pad) + + x = torch.rand(batch_size, input_dims, num_features) + out = model(x) + + assert out.size() == (batch_size, output_dims, num_features - pad * 2) diff --git a/test/test_sox_compatibility.py b/test/test_sox_compatibility.py index b6e6dbaedd..23d58a3fb1 100644 --- a/test/test_sox_compatibility.py +++ b/test/test_sox_compatibility.py @@ -1,18 +1,17 @@ import unittest import torch -from torch.testing._internal.common_utils import TestCase import torchaudio import torchaudio.functional as F import torchaudio.transforms as T from . import common_utils -from .common_utils import AudioBackendScope, BACKENDS -class TestFunctionalFiltering(TestCase): - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") +@common_utils.skipIfNoSoxBackend +class TestFunctionalFiltering(common_utils.TorchaudioTestCase): + backend = 'sox' + def test_gain(self): test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') waveform, _ = torchaudio.load(test_filepath) @@ -27,8 +26,6 @@ def test_gain(self): self.assertEqual(waveform_gain, sox_gain_waveform, atol=1e-04, rtol=1e-5) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_dither(self): test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') waveform, _ = torchaudio.load(test_filepath) @@ -49,8 +46,6 @@ def test_dither(self): self.assertEqual(waveform_dithered_noiseshaped, sox_dither_waveform_ns, atol=1e-02, rtol=1e-5) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_vctk_transform_pipeline(self): test_filepath_vctk = common_utils.get_asset_path('VCTK-Corpus', 'wav48', 'p224', 'p224_002.wav') wf_vctk, sr_vctk = torchaudio.load(test_filepath_vctk) @@ -72,8 +67,6 @@ def test_vctk_transform_pipeline(self): self.assertEqual(wf_vctk, wf_vctk_sox, rtol=1e-03, atol=1e-03) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_lowpass(self): """ Test biquad lowpass filter, compare to SoX implementation @@ -92,8 +85,6 @@ def test_lowpass(self): self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_highpass(self): """ Test biquad highpass filter, compare to SoX implementation @@ -113,8 +104,6 @@ def test_highpass(self): # TBD - this fails at the 1e-4 level, debug why self.assertEqual(output_waveform, sox_output_waveform, atol=1e-3, rtol=1e-5) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_allpass(self): """ Test biquad allpass filter, compare to SoX implementation @@ -134,8 +123,6 @@ def test_allpass(self): self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_bandpass_with_csg(self): """ Test biquad bandpass filter, compare to SoX implementation @@ -156,8 +143,6 @@ def test_bandpass_with_csg(self): self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_bandpass_without_csg(self): """ Test biquad bandpass filter, compare to SoX implementation @@ -178,8 +163,6 @@ def test_bandpass_without_csg(self): self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_bandreject(self): """ Test biquad bandreject filter, compare to SoX implementation @@ -199,8 +182,6 @@ def test_bandreject(self): self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_band_with_noise(self): """ Test biquad band filter with noise mode, compare to SoX implementation @@ -221,8 +202,6 @@ def test_band_with_noise(self): self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_band_without_noise(self): """ Test biquad band filter without noise mode, compare to SoX implementation @@ -243,8 +222,6 @@ def test_band_without_noise(self): self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_treble(self): """ Test biquad treble filter, compare to SoX implementation @@ -265,8 +242,6 @@ def test_treble(self): self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_bass(self): """ Test biquad bass filter, compare to SoX implementation @@ -287,8 +262,6 @@ def test_bass(self): self.assertEqual(output_waveform, sox_output_waveform, atol=1.5e-4, rtol=1e-5) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_deemph(self): """ Test biquad deemph filter, compare to SoX implementation @@ -305,8 +278,6 @@ def test_deemph(self): self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_riaa(self): """ Test biquad riaa filter, compare to SoX implementation @@ -323,8 +294,6 @@ def test_riaa(self): self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_contrast(self): """ Test contrast effect, compare to SoX implementation @@ -341,8 +310,6 @@ def test_contrast(self): self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_dcshift_with_limiter(self): """ Test dcshift effect, compare to SoX implementation @@ -360,8 +327,6 @@ def test_dcshift_with_limiter(self): self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_dcshift_without_limiter(self): """ Test dcshift effect, compare to SoX implementation @@ -378,8 +343,6 @@ def test_dcshift_without_limiter(self): self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_overdrive(self): """ Test overdrive effect, compare to SoX implementation @@ -397,8 +360,6 @@ def test_overdrive(self): self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_phaser_sine(self): """ Test phaser effect with sine moduldation, compare to SoX implementation @@ -419,8 +380,6 @@ def test_phaser_sine(self): self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_phaser_triangle(self): """ Test phaser effect with triangle modulation, compare to SoX implementation @@ -441,8 +400,6 @@ def test_phaser_triangle(self): self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_flanger_triangle_linear(self): """ Test flanger effect with triangle modulation and linear interpolation, compare to SoX implementation @@ -465,8 +422,6 @@ def test_flanger_triangle_linear(self): torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_flanger_triangle_quad(self): """ Test flanger effect with triangle modulation and quadratic interpolation, compare to SoX implementation @@ -489,8 +444,6 @@ def test_flanger_triangle_quad(self): torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_flanger_sine_linear(self): """ Test flanger effect with sine modulation and linear interpolation, compare to SoX implementation @@ -513,8 +466,6 @@ def test_flanger_sine_linear(self): torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_flanger_sine_quad(self): """ Test flanger effect with sine modulation and quadratic interpolation, compare to SoX implementation @@ -537,8 +488,6 @@ def test_flanger_sine_quad(self): torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_equalizer(self): """ Test biquad peaking equalizer filter, compare to SoX implementation @@ -559,8 +508,6 @@ def test_equalizer(self): self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_perf_biquad_filtering(self): fn_sine = common_utils.get_asset_path('whitenoise.wav') diff --git a/test/test_sox_effects.py b/test/test_sox_effects.py index 213477537e..68440db8d2 100644 --- a/test/test_sox_effects.py +++ b/test/test_sox_effects.py @@ -1,17 +1,18 @@ +import math import unittest + import torch import torchaudio -import math from . import common_utils -from .common_utils import AudioBackendScope, BACKENDS -class Test_SoxEffectsChain(unittest.TestCase): +@common_utils.skipIfNoSoxBackend +class Test_SoxEffectsChain(common_utils.TorchaudioTestCase): + backend = 'sox' + test_filepath = common_utils.get_asset_path("steam-train-whistle-daniel_simon.mp3") - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_single_channel(self): fn_sine = common_utils.get_asset_path("sinewave.wav") E = torchaudio.sox_effects.SoxEffectsChain() @@ -21,8 +22,6 @@ def test_single_channel(self): # check if effects worked # print(x.size()) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_rate_channels(self): target_rate = 16000 target_channels = 1 @@ -35,8 +34,6 @@ def test_rate_channels(self): self.assertEqual(sr, target_rate) self.assertEqual(x.size(0), target_channels) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_lowpass_speed(self): speed = .8 si, _ = torchaudio.info(self.test_filepath) @@ -49,8 +46,6 @@ def test_lowpass_speed(self): # check if effects worked self.assertEqual(x.size(1), int((si.length / si.channels) / speed)) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_ulaw_and_siginfo(self): si_out = torchaudio.sox_signalinfo_t() ei_out = torchaudio.sox_encodinginfo_t() @@ -68,8 +63,6 @@ def test_ulaw_and_siginfo(self): self.assertLess(x.unique().size(0), 2**8 + 1) self.assertEqual(x.numel(), si_in.length) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_band_chorus(self): si_in, ei_in = torchaudio.info(self.test_filepath) ei_in.encoding = torchaudio.get_sox_encoding_t(1) @@ -84,8 +77,6 @@ def test_band_chorus(self): self.assertEqual(x.size(0), si_in.channels) self.assertGreaterEqual(x.size(1) * x.size(0), si_in.length) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_synth(self): si_in, ei_in = torchaudio.info(self.test_filepath) len_in_seconds = si_in.length / si_in.channels / si_in.rate @@ -99,8 +90,6 @@ def test_synth(self): self.assertEqual(x.size(0), si_in.channels) self.assertEqual(si_in.length, x.size(0) * x.size(1)) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_gain(self): E = torchaudio.sox_effects.SoxEffectsChain() E.set_input_file(self.test_filepath) @@ -124,8 +113,6 @@ def test_gain(self): E.clear_chain() self.assertLess(x.abs().max().item(), 1.) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_tempo_or_speed(self): tempo = .8 si, _ = torchaudio.info(self.test_filepath) @@ -159,8 +146,6 @@ def test_tempo_or_speed(self): # check if effect worked self.assertAlmostEqual(x.size(1), math.ceil((si.length / si.channels) / speed), delta=1) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_trim(self): x_orig, _ = torchaudio.load(self.test_filepath) offset = "10000s" @@ -174,8 +159,6 @@ def test_trim(self): # check if effect worked self.assertTrue(x.allclose(x_orig[:, offset_int:(offset_int + num_frames_int)], rtol=1e-4, atol=1e-4)) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_silence_contrast(self): si, _ = torchaudio.info(self.test_filepath) E = torchaudio.sox_effects.SoxEffectsChain() @@ -186,8 +169,6 @@ def test_silence_contrast(self): # check if effect worked self.assertLess(x.numel(), si.length) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_reverse(self): x_orig, _ = torchaudio.load(self.test_filepath) E = torchaudio.sox_effects.SoxEffectsChain() @@ -198,8 +179,6 @@ def test_reverse(self): rev_idx = torch.LongTensor(range(x_orig.size(1))[::-1]) self.assertTrue(x_orig.allclose(x_rev[:, rev_idx], rtol=1e-5, atol=2e-5)) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_compand_fade(self): E = torchaudio.sox_effects.SoxEffectsChain() E.set_input_file(self.test_filepath) @@ -209,8 +188,6 @@ def test_compand_fade(self): # check if effect worked # print(x.size()) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_biquad_delay(self): si, _ = torchaudio.info(self.test_filepath) E = torchaudio.sox_effects.SoxEffectsChain() @@ -222,8 +199,6 @@ def test_biquad_delay(self): # check if effect worked self.assertTrue(x.size(1) == (si.length / si.channels) + 15000) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_invalid_effect_name(self): E = torchaudio.sox_effects.SoxEffectsChain() E.set_input_file(self.test_filepath) @@ -231,8 +206,6 @@ def test_invalid_effect_name(self): with self.assertRaises(LookupError): E.append_effect_to_chain("special", [""]) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_unimplemented_effect(self): E = torchaudio.sox_effects.SoxEffectsChain() E.set_input_file(self.test_filepath) @@ -240,8 +213,6 @@ def test_unimplemented_effect(self): with self.assertRaises(NotImplementedError): E.append_effect_to_chain("spectrogram", [""]) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_invalid_effect_options(self): E = torchaudio.sox_effects.SoxEffectsChain() E.set_input_file(self.test_filepath) @@ -250,8 +221,6 @@ def test_invalid_effect_options(self): with self.assertRaises(RuntimeError): E.sox_build_flow_effects() - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_fade(self): x_orig, _ = torchaudio.load(self.test_filepath) fade_in_len = 44100 @@ -268,8 +237,6 @@ def test_fade(self): # check if effect worked self.assertTrue(x.allclose(fade(x_orig), rtol=1e-4, atol=1e-4)) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_vol(self): x_orig, _ = torchaudio.load(self.test_filepath) @@ -284,8 +251,6 @@ def test_vol(self): # check if effect worked self.assertTrue(x.allclose(z, rtol=1e-4, atol=1e-4)) - @unittest.skipIf("sox" not in BACKENDS, "sox not available") - @AudioBackendScope("sox") def test_vad(self): sample_files = [ common_utils.get_asset_path("vad-go-stereo-44100.wav"), diff --git a/test/test_transforms.py b/test/test_transforms.py index ad8a55fb4b..af2b834fdb 100644 --- a/test/test_transforms.py +++ b/test/test_transforms.py @@ -2,7 +2,6 @@ import unittest import torch -from torch.testing._internal.common_utils import TestCase import torchaudio import torchaudio.transforms as transforms import torchaudio.functional as F @@ -10,7 +9,8 @@ from . import common_utils -class Tester(TestCase): +class Tester(common_utils.TorchaudioTestCase): + backend = 'default' # create a sinewave signal for testing sample_rate = 16000 diff --git a/test/torchscript_consistency_cpu_test.py b/test/torchscript_consistency_cpu_test.py index 81e6616821..38fe7961fd 100644 --- a/test/torchscript_consistency_cpu_test.py +++ b/test/torchscript_consistency_cpu_test.py @@ -1,14 +1,24 @@ -from parameterized import parameterized_class +import torch -from .common_utils import TestCase, common_test_class_parameters +from . import common_utils from .torchscript_consistency_impl import Functional, Transforms -parameters = list(common_test_class_parameters(devices=['cpu'])) -@parameterized_class(parameters) -class TestFunctional(Functional, TestCase): - pass +class TestFunctionalFloat32(Functional, common_utils.PytorchTestCase): + dtype = torch.float32 + device = torch.device('cpu') -@parameterized_class(parameters) -class TestTransforms(Transforms, TestCase): - pass + +class TestFunctionalFloat64(Functional, common_utils.PytorchTestCase): + dtype = torch.float64 + device = torch.device('cpu') + + +class TestTransformsFloat32(Transforms, common_utils.PytorchTestCase): + dtype = torch.float32 + device = torch.device('cpu') + + +class TestTransformsFloat64(Transforms, common_utils.PytorchTestCase): + dtype = torch.float64 + device = torch.device('cpu') diff --git a/test/torchscript_consistency_cuda_test.py b/test/torchscript_consistency_cuda_test.py index 0899ba2066..b317334ce4 100644 --- a/test/torchscript_consistency_cuda_test.py +++ b/test/torchscript_consistency_cuda_test.py @@ -1,5 +1,28 @@ -from .common_utils import define_test_suites +import torch + +from . import common_utils from .torchscript_consistency_impl import Functional, Transforms -define_test_suites(globals(), [Functional, Transforms], devices=['cuda']) +@common_utils.skipIfNoCuda +class TestFunctionalFloat32(Functional, common_utils.PytorchTestCase): + dtype = torch.float32 + device = torch.device('cuda') + + +@common_utils.skipIfNoCuda +class TestFunctionalFloat64(Functional, common_utils.PytorchTestCase): + dtype = torch.float64 + device = torch.device('cuda') + + +@common_utils.skipIfNoCuda +class TestTransformsFloat32(Transforms, common_utils.PytorchTestCase): + dtype = torch.float32 + device = torch.device('cuda') + + +@common_utils.skipIfNoCuda +class TestTransformsFloat64(Transforms, common_utils.PytorchTestCase): + dtype = torch.float64 + device = torch.device('cuda') diff --git a/test/torchscript_consistency_impl.py b/test/torchscript_consistency_impl.py index a84b629bd7..2527cd5fbf 100644 --- a/test/torchscript_consistency_impl.py +++ b/test/torchscript_consistency_impl.py @@ -616,5 +616,6 @@ def test_SlidingWindowCmn(self): def test_Vad(self): filepath = common_utils.get_asset_path("vad-go-mono-32000.wav") + common_utils.set_audio_backend('default') waveform, sample_rate = torchaudio.load(filepath) self._assert_consistency(T.Vad(sample_rate=sample_rate), waveform) diff --git a/torchaudio/__init__.py b/torchaudio/__init__.py index a08eaf6964..16419ff3ff 100644 --- a/torchaudio/__init__.py +++ b/torchaudio/__init__.py @@ -1,10 +1,5 @@ -import atexit -import os.path -from pathlib import Path -from typing import Any, Callable, Optional, Tuple, Union - -import torch -from torch import Tensor +from . import extension +from torchaudio._internal import module_utils as _mod_utils from torchaudio import ( compliance, datasets, @@ -12,14 +7,23 @@ sox_effects, transforms ) -from torchaudio._backend import ( - check_input, - _audio_backend_guard, - _get_audio_backend_module, +from torchaudio.backend import ( + list_audio_backends, get_audio_backend, set_audio_backend, + save_encinfo, + sox_signalinfo_t, + sox_encodinginfo_t, + get_sox_option_t, + get_sox_encoding_t, + get_sox_bool, + SignalInfo, + EncodingInfo, +) +from torchaudio.sox_effects import ( + init_sox_effects as _init_sox_effects, + shutdown_sox_effects as _shutdown_sox_effects, ) -from torchaudio._soundfile_backend import SignalInfo, EncodingInfo try: from .version import __version__, git_version # noqa: F401 @@ -27,397 +31,25 @@ pass -def load(filepath: Union[str, Path], - out: Optional[Tensor] = None, - normalization: Union[bool, float, Callable] = True, - channels_first: bool = True, - num_frames: int = 0, - offset: int = 0, - signalinfo: Optional[SignalInfo] = None, - encodinginfo: Optional[EncodingInfo] = None, - filetype: Optional[str] = None) -> Tuple[Tensor, int]: - r"""Loads an audio file from disk into a tensor - - Args: - filepath (str or Path): Path to audio file - out (Tensor, optional): An output tensor to use instead of creating one. (Default: ``None``) - normalization (bool, float, or callable, optional): If boolean `True`, then output is divided by `1 << 31` - (assumes signed 32-bit audio), and normalizes to `[-1, 1]`. - If `float`, then output is divided by that number - If `Callable`, then the output is passed as a parameter - to the given function, then the output is divided by - the result. (Default: ``True``) - channels_first (bool, optional): Set channels first or length first in result. (Default: ``True``) - num_frames (int, optional): Number of frames to load. 0 to load everything after the offset. - (Default: ``0``) - offset (int, optional): Number of frames from the start of the file to begin data loading. - (Default: ``0``) - signalinfo (sox_signalinfo_t, optional): A sox_signalinfo_t type, which could be helpful if the - audio type cannot be automatically determined. (Default: ``None``) - encodinginfo (sox_encodinginfo_t, optional): A sox_encodinginfo_t type, which could be set if the - audio type cannot be automatically determined. (Default: ``None``) - filetype (str, optional): A filetype or extension to be set if sox cannot determine it - automatically. (Default: ``None``) - - Returns: - (Tensor, int): An output tensor of size `[C x L]` or `[L x C]` where L is the number - of audio frames and C is the number of channels. An integer which is the sample rate of the - audio (as listed in the metadata of the file) - - Example - >>> data, sample_rate = torchaudio.load('foo.mp3') - >>> print(data.size()) - torch.Size([2, 278756]) - >>> print(sample_rate) - 44100 - >>> data_vol_normalized, _ = torchaudio.load('foo.mp3', normalization=lambda x: torch.abs(x).max()) - >>> print(data_vol_normalized.abs().max()) - 1. - - """ - - return _get_audio_backend_module().load( - filepath, - out=out, - normalization=normalization, - channels_first=channels_first, - num_frames=num_frames, - offset=offset, - signalinfo=signalinfo, - encodinginfo=encodinginfo, - filetype=filetype, - ) - - -def load_wav(filepath: Union[str, Path], **kwargs: Any) -> Tuple[Tensor, int]: - r""" Loads a wave file. It assumes that the wav file uses 16 bit per sample that needs normalization by shifting - the input right by 16 bits. - - Args: - filepath (str or Path): Path to audio file - - Returns: - (Tensor, int): An output tensor of size `[C x L]` or `[L x C]` where L is the number - of audio frames and C is the number of channels. An integer which is the sample rate of the - audio (as listed in the metadata of the file) - """ - kwargs['normalization'] = 1 << 16 - return load(filepath, **kwargs) - - -def save(filepath: str, src: Tensor, sample_rate: int, precision: int = 16, channels_first: bool = True) -> None: - r"""Convenience function for `save_encinfo`. - - Args: - filepath (str): Path to audio file - src (Tensor): An input 2D tensor of shape `[C x L]` or `[L x C]` where L is - the number of audio frames, C is the number of channels - sample_rate (int): An integer which is the sample rate of the - audio (as listed in the metadata of the file) - precision (int, optional): Bit precision (Default: ``16``) - channels_first (bool, optional): Set channels first or length first in result. ( - Default: ``True``) - """ - - return _get_audio_backend_module().save( - filepath, src, sample_rate, precision=precision, channels_first=channels_first - ) - - -@_audio_backend_guard("sox") -def save_encinfo(filepath: str, - src: Tensor, - channels_first: bool = True, - signalinfo: Optional[SignalInfo] = None, - encodinginfo: Optional[EncodingInfo] = None, - filetype: Optional[str] = None) -> None: - r"""Saves a tensor of an audio signal to disk as a standard format like mp3, wav, etc. - - Args: - filepath (str): Path to audio file - src (Tensor): An input 2D tensor of shape `[C x L]` or `[L x C]` where L is - the number of audio frames, C is the number of channels - channels_first (bool, optional): Set channels first or length first in result. (Default: ``True``) - signalinfo (sox_signalinfo_t, optional): A sox_signalinfo_t type, which could be helpful if the - audio type cannot be automatically determined (Default: ``None``). - encodinginfo (sox_encodinginfo_t, optional): A sox_encodinginfo_t type, which could be set if the - audio type cannot be automatically determined (Default: ``None``). - filetype (str, optional): A filetype or extension to be set if sox cannot determine it - automatically. (Default: ``None``) - - Example - >>> data, sample_rate = torchaudio.load('foo.mp3') - >>> torchaudio.save('foo.wav', data, sample_rate) - - """ - ch_idx, len_idx = (0, 1) if channels_first else (1, 0) - - # check if save directory exists - abs_dirpath = os.path.dirname(os.path.abspath(filepath)) - if not os.path.isdir(abs_dirpath): - raise OSError("Directory does not exist: {}".format(abs_dirpath)) - # check that src is a CPU tensor - check_input(src) - # Check/Fix shape of source data - if src.dim() == 1: - # 1d tensors as assumed to be mono signals - src.unsqueeze_(ch_idx) - elif src.dim() > 2 or src.size(ch_idx) > 16: - # assumes num_channels < 16 - raise ValueError( - "Expected format where C < 16, but found {}".format(src.size())) - # sox stores the sample rate as a float, though practically sample rates are almost always integers - # convert integers to floats - if signalinfo: - if signalinfo.rate and not isinstance(signalinfo.rate, float): - if float(signalinfo.rate) == signalinfo.rate: - signalinfo.rate = float(signalinfo.rate) - else: - raise TypeError('Sample rate should be a float or int') - # check if the bit precision (i.e. bits per sample) is an integer - if signalinfo.precision and not isinstance(signalinfo.precision, int): - if int(signalinfo.precision) == signalinfo.precision: - signalinfo.precision = int(signalinfo.precision) - else: - raise TypeError('Bit precision should be an integer') - # programs such as librosa normalize the signal, unnormalize if detected - if src.min() >= -1.0 and src.max() <= 1.0: - src = src * (1 << 31) - src = src.long() - # set filetype and allow for files with no extensions - extension = os.path.splitext(filepath)[1] - filetype = extension[1:] if len(extension) > 0 else filetype - # transpose from C x L -> L x C - if channels_first: - src = src.transpose(1, 0) - # save data to file - src = src.contiguous() - - from . import _torchaudio - _torchaudio.write_audio_file(filepath, src, signalinfo, encodinginfo, filetype) - - -def info(filepath: str) -> Tuple[SignalInfo, EncodingInfo]: - r"""Gets metadata from an audio file without loading the signal. - - Args: - filepath (str): Path to audio file - - Returns: - (sox_signalinfo_t, sox_encodinginfo_t): A si (sox_signalinfo_t) signal - info as a python object. An ei (sox_encodinginfo_t) encoding info - - Example - >>> si, ei = torchaudio.info('foo.wav') - >>> rate, channels, encoding = si.rate, si.channels, ei.encoding - """ - - return _get_audio_backend_module().info(filepath) - - -@_audio_backend_guard("sox") -def sox_signalinfo_t() -> SignalInfo: - r"""Create a sox_signalinfo_t object. This object can be used to set the sample - rate, number of channels, length, bit precision and headroom multiplier - primarily for effects - - Returns: sox_signalinfo_t(object) - - rate (float), sample rate as a float, practically will likely be an integer float - - channel (int), number of audio channels - - precision (int), bit precision - - length (int), length of audio in samples * channels, 0 for unspecified and -1 for unknown - - mult (float, optional), headroom multiplier for effects and ``None`` for no multiplier - - Example - >>> si = torchaudio.sox_signalinfo_t() - >>> si.channels = 1 - >>> si.rate = 16000. - >>> si.precision = 16 - >>> si.length = 0 - """ - - from . import _torchaudio - return _torchaudio.sox_signalinfo_t() - - -@_audio_backend_guard("sox") -def sox_encodinginfo_t() -> EncodingInfo: - r"""Create a sox_encodinginfo_t object. This object can be used to set the encoding - type, bit precision, compression factor, reverse bytes, reverse nibbles, - reverse bits and endianness. This can be used in an effects chain to encode the - final output or to save a file with a specific encoding. For example, one could - use the sox ulaw encoding to do 8-bit ulaw encoding. Note in a tensor output - the result will be a 32-bit number, but number of unique values will be determined by - the bit precision. - - Returns: sox_encodinginfo_t(object) - - encoding (sox_encoding_t), output encoding - - bits_per_sample (int), bit precision, same as `precision` in sox_signalinfo_t - - compression (float), compression for lossy formats, 0.0 for default compression - - reverse_bytes (sox_option_t), reverse bytes, use sox_option_default - - reverse_nibbles (sox_option_t), reverse nibbles, use sox_option_default - - reverse_bits (sox_option_t), reverse bytes, use sox_option_default - - opposite_endian (sox_bool), change endianness, use sox_false - - Example - >>> ei = torchaudio.sox_encodinginfo_t() - >>> ei.encoding = torchaudio.get_sox_encoding_t(1) - >>> ei.bits_per_sample = 16 - >>> ei.compression = 0 - >>> ei.reverse_bytes = torchaudio.get_sox_option_t(2) - >>> ei.reverse_nibbles = torchaudio.get_sox_option_t(2) - >>> ei.reverse_bits = torchaudio.get_sox_option_t(2) - >>> ei.opposite_endian = torchaudio.get_sox_bool(0) - - """ - - from . import _torchaudio - ei = _torchaudio.sox_encodinginfo_t() - sdo = get_sox_option_t(2) # sox_default_option - ei.reverse_bytes = sdo - ei.reverse_nibbles = sdo - ei.reverse_bits = sdo - return ei - - -@_audio_backend_guard("sox") -def get_sox_encoding_t(i: int = None) -> EncodingInfo: - r"""Get enum of sox_encoding_t for sox encodings. - - Args: - i (int, optional): Choose type or get a dict with all possible options - use ``__members__`` to see all options when not specified. (Default: ``None``) - - Returns: - sox_encoding_t: A sox_encoding_t type for output encoding - """ - - from . import _torchaudio - if i is None: - # one can see all possible values using the .__members__ attribute - return _torchaudio.sox_encoding_t - else: - return _torchaudio.sox_encoding_t(i) - - -@_audio_backend_guard("sox") -def get_sox_option_t(i: int = 2) -> Any: - r"""Get enum of sox_option_t for sox encodinginfo options. - - Args: - i (int, optional): Choose type or get a dict with all possible options - use ``__members__`` to see all options when not specified. - (Default: ``sox_option_default`` or ``2``) - Returns: - sox_option_t: A sox_option_t type - """ - - from . import _torchaudio - if i is None: - return _torchaudio.sox_option_t - else: - return _torchaudio.sox_option_t(i) - - -@_audio_backend_guard("sox") -def get_sox_bool(i: int = 0) -> Any: - r"""Get enum of sox_bool for sox encodinginfo options. - - Args: - i (int, optional): Choose type or get a dict with all possible options - use ``__members__`` to see all options when not specified. (Default: - ``sox_false`` or ``0``) - - Returns: - sox_bool: A sox_bool type - """ - - from . import _torchaudio - if i is None: - return _torchaudio.sox_bool - else: - return _torchaudio.sox_bool(i) - - -_SOX_INITIALIZED: Optional[bool] = False -# This variable has a micro lifecycle. (False -> True -> None) -# False: Not initialized -# True: Initialized -# None: Already shut down (should not be initialized again.) - -_SOX_SUCCESS_CODE = 0 -# defined at -# https://fossies.org/dox/sox-14.4.2/sox_8h.html#a8e07e80cebeff3339265d89c387cea93a9ef2b87ec303edfe40751d9a85fadeeb - - -@_audio_backend_guard("sox") +@_mod_utils.deprecated( + "Please remove the function call to initialize_sox. " + "Resource initialization is now automatically handled.") def initialize_sox() -> int: - """Initialize sox for use with effects chains. - - You only need to call this function once to use SoX effects chains multiple times. - It is safe to call this function multiple times as long as ``shutdown_sox`` is not yet called. - Once ``shutdown_sox`` is called, you can no longer use SoX effects and calling this function - results in `RuntimeError`. + """Initialize sox effects. - Note: - This function is not required for simple loading. - - Returns: - int: Code corresponding to sox_error_t enum. See - https://fossies.org/dox/sox-14.4.2/sox_8h.html#a8e07e80cebeff3339265d89c387cea93 + This function is deprecated. See ``torchaudio.sox_effects.init_sox_effects`` """ - global _SOX_INITIALIZED - if _SOX_INITIALIZED is None: - raise RuntimeError('SoX effects chain has been already shut down. Can not initialize again.') - if not _SOX_INITIALIZED: - from . import _torchaudio - code = _torchaudio.initialize_sox() - if code == _SOX_SUCCESS_CODE: - _SOX_INITIALIZED = True - atexit.register(shutdown_sox) - return code - return _SOX_SUCCESS_CODE - - -@_audio_backend_guard("sox") -def shutdown_sox() -> int: - """Showdown sox for effects chain. + _init_sox_effects() - You do not need to call this function as it will be called automatically - at the end of program execution, if ``initialize_sox`` was called. - It is safe to call this function multiple times. +@_mod_utils.deprecated( + "Please remove the function call to torchaudio.shutdown_sox. " + "Resource clean up is now automatically handled. " + "In the unlikely event that you need to manually shutdown sox, " + "please use torchaudio.sox_effects.shutdown_sox_effects.") +def shutdown_sox(): + """Shutdown sox effects. - Returns: - int: Code corresponding to sox_error_t enum. See - https://fossies.org/dox/sox-14.4.2/sox_8h.html#a8e07e80cebeff3339265d89c387cea93 + This function is deprecated. See ``torchaudio.sox_effects.shutdown_sox_effects`` """ - global _SOX_INITIALIZED - if _SOX_INITIALIZED: - from . import _torchaudio - code = _torchaudio.shutdown_sox() - if code == _SOX_INITIALIZED: - _SOX_INITIALIZED = None - return code - return _SOX_SUCCESS_CODE - - -def _audio_normalization(signal: Tensor, normalization: Union[bool, float, Callable]) -> None: - """Audio normalization of a tensor in-place. The normalization can be a bool, - a number, or a callable that takes the audio tensor as an input. SoX uses - 32-bit signed integers internally, thus bool normalizes based on that assumption. - """ - - if not normalization: - return - - if isinstance(normalization, bool): - normalization = 1 << 31 - - if isinstance(normalization, (float, int)): - # normalize with custom value - a = normalization - signal /= a - elif callable(normalization): - a = normalization(signal) - signal /= a + _shutdown_sox_effects() diff --git a/torchaudio/_backend.py b/torchaudio/_backend.py deleted file mode 100644 index f5646d7970..0000000000 --- a/torchaudio/_backend.py +++ /dev/null @@ -1,68 +0,0 @@ -from functools import wraps -from typing import Any, List, Union - -import platform -import torch -from torch import Tensor - -from . import _soundfile_backend, _sox_backend - - -if platform.system() == "Windows": - _audio_backend = "soundfile" - _audio_backends = {"soundfile": _soundfile_backend} -else: - _audio_backend = "sox" - _audio_backends = {"sox": _sox_backend, "soundfile": _soundfile_backend} - - -def set_audio_backend(backend: str) -> None: - """ - Specifies the package used to load. - Args: - backend (str): Name of the backend. One of {}. - """.format(_audio_backends.keys()) - global _audio_backend - if backend not in _audio_backends: - raise ValueError( - "Invalid backend '{}'. Options are {}.".format(backend, _audio_backends.keys()) - ) - _audio_backend = backend - - -def get_audio_backend() -> str: - """ - Gets the name of the package used to load. - """ - return _audio_backend - - -def _get_audio_backend_module() -> Any: - """ - Gets the module backend to load. - """ - backend = get_audio_backend() - return _audio_backends[backend] - - -def _audio_backend_guard(backends: Union[str, List[str]]) -> Any: - - if isinstance(backends, str): - backends = [backends] - - def decorator(func): - @wraps(func) - def wrapper(*args, **kwargs): - if get_audio_backend() not in backends: - raise RuntimeError("Function {} requires backend to be one of {}.".format(func.__name__, backends)) - return func(*args, **kwargs) - return wrapper - - return decorator - - -def check_input(src: Tensor) -> None: - if not torch.is_tensor(src): - raise TypeError('Expected a tensor, got %s' % type(src)) - if src.is_cuda: - raise TypeError('Expected a CPU based tensor, got %s' % type(src)) diff --git a/torchaudio/_internal/__init__.py b/torchaudio/_internal/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/torchaudio/_internal/misc_ops.py b/torchaudio/_internal/misc_ops.py new file mode 100644 index 0000000000..b67b8d1598 --- /dev/null +++ b/torchaudio/_internal/misc_ops.py @@ -0,0 +1,30 @@ +from typing import Union, Callable + +import torch +from torch import Tensor + + +def normalize_audio(signal: Tensor, normalization: Union[bool, float, Callable]) -> None: + """Audio normalization of a tensor in-place. The normalization can be a bool, + a number, or a callable that takes the audio tensor as an input. SoX uses + 32-bit signed integers internally, thus bool normalizes based on that assumption. + """ + + if not normalization: + return + + if isinstance(normalization, bool): + normalization = 1 << 31 + + if isinstance(normalization, (float, int)): + # normalize with custom value + signal /= normalization + elif callable(normalization): + signal /= normalization(signal) + + +def check_input(src: Tensor) -> None: + if not torch.is_tensor(src): + raise TypeError('Expected a tensor, got %s' % type(src)) + if src.is_cuda: + raise TypeError('Expected a CPU based tensor, got %s' % type(src)) diff --git a/torchaudio/_internal/module_utils.py b/torchaudio/_internal/module_utils.py new file mode 100644 index 0000000000..575a76c1ed --- /dev/null +++ b/torchaudio/_internal/module_utils.py @@ -0,0 +1,56 @@ +import warnings +import importlib.util +from typing import Optional +from functools import wraps + + +def is_module_available(*modules: str) -> bool: + r"""Returns if a top-level module with :attr:`name` exists *without** + importing it. This is generally safer than try-catch block around a + `import X`. It avoids third party libraries breaking assumptions of some of + our tests, e.g., setting multiprocessing start method when imported + (see librosa/#747, torchvision/#544). + """ + return all(importlib.util.find_spec(m) is not None for m in modules) + + +def requires_module(*modules: str): + """Decorate function to give error message if invoked without required optional modules. + + This decorator is to give better error message to users rather + than raising ``NameError: name 'module' is not defined`` at random places. + """ + missing = [m for m in modules if not is_module_available(m)] + + if not missing: + # fall through. If all the modules are available, no need to decorate + def decorator(func): + return func + else: + req = f'module: {missing[0]}' if len(missing) == 1 else f'modules: {missing}' + + def decorator(func): + @wraps(func) + def wrapped(*args, **kwargs): + raise RuntimeError(f'{func.__module__}.{func.__name__} requires {req}') + return wrapped + return decorator + + +def deprecated(direction: str, version: Optional[str] = None): + """Decorator to add deprecation message + + Args: + direction: Migration steps to be given to users. + """ + def decorator(func): + @wraps(func) + def wrapped(*args, **kwargs): + message = ( + f'{func.__module__}.{func.__name__} has been deprecated ' + f'and will be removed from {"future" if version is None else version} release.' + f'{direction}') + warnings.warn(message, stacklevel=2) + return func(*args, **kwargs) + return wrapped + return decorator diff --git a/torchaudio/_sox_backend.py b/torchaudio/_sox_backend.py deleted file mode 100644 index 3478d30d50..0000000000 --- a/torchaudio/_sox_backend.py +++ /dev/null @@ -1,72 +0,0 @@ -import os.path -from typing import Optional, Tuple - -import torch -from torch import Tensor -import torchaudio -from torchaudio._soundfile_backend import SignalInfo, EncodingInfo - - -def load(filepath: str, - out: Optional[Tensor] = None, - normalization: bool = True, - channels_first: bool = True, - num_frames: int = 0, - offset: int = 0, - signalinfo: SignalInfo = None, - encodinginfo: EncodingInfo = None, - filetype: Optional[str] = None) -> Tuple[Tensor, int]: - r"""See torchaudio.load""" - - # stringify if `pathlib.Path` (noop if already `str`) - filepath = str(filepath) - # check if valid file - if not os.path.isfile(filepath): - raise OSError("{} not found or is a directory".format(filepath)) - - # initialize output tensor - if out is not None: - torchaudio.check_input(out) - else: - out = torch.FloatTensor() - - if num_frames < -1: - raise ValueError("Expected value for num_samples -1 (entire file) or >=0") - if offset < 0: - raise ValueError("Expected positive offset value") - - from . import _torchaudio - sample_rate = _torchaudio.read_audio_file( - filepath, - out, - channels_first, - num_frames, - offset, - signalinfo, - encodinginfo, - filetype - ) - - # normalize if needed - torchaudio._audio_normalization(out, normalization) - - return out, sample_rate - - -def save(filepath: str, src: Tensor, sample_rate: int, precision: int = 16, channels_first: bool = True) -> None: - r"""See torchaudio.save""" - - si = torchaudio.sox_signalinfo_t() - ch_idx = 0 if channels_first else 1 - si.rate = sample_rate - si.channels = 1 if src.dim() == 1 else src.size(ch_idx) - si.length = src.numel() - si.precision = precision - return torchaudio.save_encinfo(filepath, src, channels_first, si) - - -def info(filepath: str) -> Tuple[SignalInfo, EncodingInfo]: - r"""See torchaudio.info""" - - from . import _torchaudio - return _torchaudio.get_info(filepath) diff --git a/torchaudio/backend/__init__.py b/torchaudio/backend/__init__.py new file mode 100644 index 0000000000..0812c09cf4 --- /dev/null +++ b/torchaudio/backend/__init__.py @@ -0,0 +1,21 @@ +from . import utils +from .utils import ( + list_audio_backends, + get_audio_backend, + set_audio_backend, +) +from .sox_backend import ( + save_encinfo, + sox_signalinfo_t, + sox_encodinginfo_t, + get_sox_option_t, + get_sox_encoding_t, + get_sox_bool, +) +from .common import ( + SignalInfo, + EncodingInfo, +) + + +utils._init_audio_backend() diff --git a/torchaudio/backend/common.py b/torchaudio/backend/common.py new file mode 100644 index 0000000000..0593c34bd5 --- /dev/null +++ b/torchaudio/backend/common.py @@ -0,0 +1,143 @@ +from typing import Any, Optional + + +class SignalInfo: + def __init__(self, + channels: Optional[int] = None, + rate: Optional[float] = None, + precision: Optional[int] = None, + length: Optional[int] = None) -> None: + self.channels = channels + self.rate = rate + self.precision = precision + self.length = length + + +class EncodingInfo: + def __init__(self, + encoding: Any = None, + bits_per_sample: Optional[int] = None, + compression: Optional[float] = None, + reverse_bytes: Any = None, + reverse_nibbles: Any = None, + reverse_bits: Any = None, + opposite_endian: Optional[bool] = None) -> None: + self.encoding = encoding + self.bits_per_sample = bits_per_sample + self.compression = compression + self.reverse_bytes = reverse_bytes + self.reverse_nibbles = reverse_nibbles + self.reverse_bits = reverse_bits + self.opposite_endian = opposite_endian + + +_LOAD_DOCSTRING = r"""Loads an audio file from disk into a tensor + +Args: + filepath: Path to audio file + + out: An optional output tensor to use instead of creating one. (Default: ``None``) + + normalization: Optional normalization. + If boolean `True`, then output is divided by `1 << 31`. + Assuming the input is signed 32-bit audio, this normalizes to `[-1, 1]`. + If `float`, then output is divided by that number. + If `Callable`, then the output is passed as a paramete to the given function, + then the output is divided by the result. (Default: ``True``) + + channels_first: Set channels first or length first in result. (Default: ``True``) + + num_frames: Number of frames to load. 0 to load everything after the offset. + (Default: ``0``) + + offset: Number of frames from the start of the file to begin data loading. + (Default: ``0``) + + signalinfo: A sox_signalinfo_t type, which could be helpful if the + audio type cannot be automatically determined. (Default: ``None``) + + encodinginfo: A sox_encodinginfo_t type, which could be set if the + audio type cannot be automatically determined. (Default: ``None``) + + filetype: A filetype or extension to be set if sox cannot determine it + automatically. (Default: ``None``) + +Returns: + (Tensor, int): An output tensor of size `[C x L]` or `[L x C]` where + L is the number of audio frames and + C is the number of channels. + An integer which is the sample rate of the audio (as listed in the metadata of the file) + +Example + >>> data, sample_rate = torchaudio.load('foo.mp3') + >>> print(data.size()) + torch.Size([2, 278756]) + >>> print(sample_rate) + 44100 + >>> data_vol_normalized, _ = torchaudio.load('foo.mp3', normalization=lambda x: torch.abs(x).max()) + >>> print(data_vol_normalized.abs().max()) + 1. +""" + + +_LOAD_WAV_DOCSTRING = r""" Loads a wave file. + +It assumes that the wav file uses 16 bit per sample that needs normalization by +shifting the input right by 16 bits. + +Args: + filepath: Path to audio file + +Returns: + (Tensor, int): An output tensor of size `[C x L]` or `[L x C]` where L is the number + of audio frames and C is the number of channels. An integer which is the sample rate of the + audio (as listed in the metadata of the file) +""" + +_SAVE_DOCSTRING = r"""Saves a Tensor on file as an audio file + +Args: + filepath: Path to audio file + src: An input 2D tensor of shape `[C x L]` or `[L x C]` where L is + the number of audio frames, C is the number of channels + sample_rate: An integer which is the sample rate of the + audio (as listed in the metadata of the file) + precision Bit precision (Default: ``16``) + channels_first (bool, optional): Set channels first or length first in result. ( + Default: ``True``) +""" + + +_INFO_DOCSTRING = r"""Gets metadata from an audio file without loading the signal. + +Args: + filepath: Path to audio file + +Returns: + (sox_signalinfo_t, sox_encodinginfo_t): A si (sox_signalinfo_t) signal + info as a python object. An ei (sox_encodinginfo_t) encoding info + +Example + >>> si, ei = torchaudio.info('foo.wav') + >>> rate, channels, encoding = si.rate, si.channels, ei.encoding +""" + + +def _impl_load(func): + setattr(func, '__doc__', _LOAD_DOCSTRING) + return func + + +def _impl_load_wav(func): + setattr(func, '__doc__', _LOAD_WAV_DOCSTRING) + return func + + +def _impl_save(func): + setattr(func, '__doc__', _SAVE_DOCSTRING) + return func + + +def _impl_info(func): + setattr(func, '__doc__', _INFO_DOCSTRING) + return func diff --git a/torchaudio/backend/no_backend.py b/torchaudio/backend/no_backend.py new file mode 100644 index 0000000000..60571c34a9 --- /dev/null +++ b/torchaudio/backend/no_backend.py @@ -0,0 +1,35 @@ +from pathlib import Path +from typing import Any, Callable, Optional, Tuple, Union + +from torch import Tensor + +from . import common +from .common import SignalInfo, EncodingInfo + + +@common._impl_load +def load(filepath: Union[str, Path], + out: Optional[Tensor] = None, + normalization: Union[bool, float, Callable] = True, + channels_first: bool = True, + num_frames: int = 0, + offset: int = 0, + signalinfo: Optional[SignalInfo] = None, + encodinginfo: Optional[EncodingInfo] = None, + filetype: Optional[str] = None) -> Tuple[Tensor, int]: + raise RuntimeError('No audio I/O backend is available.') + + +@common._impl_load_wav +def load_wav(filepath: Union[str, Path], **kwargs: Any) -> Tuple[Tensor, int]: + raise RuntimeError('No audio I/O backend is available.') + + +@common._impl_save +def save(filepath: str, src: Tensor, sample_rate: int, precision: int = 16, channels_first: bool = True) -> None: + raise RuntimeError('No audio I/O backend is available.') + + +@common._impl_info +def info(filepath: str) -> Tuple[SignalInfo, EncodingInfo]: + raise RuntimeError('No audio I/O backend is available.') diff --git a/torchaudio/_soundfile_backend.py b/torchaudio/backend/soundfile_backend.py similarity index 67% rename from torchaudio/_soundfile_backend.py rename to torchaudio/backend/soundfile_backend.py index 1a88ba5e80..680264bcd6 100644 --- a/torchaudio/_soundfile_backend.py +++ b/torchaudio/backend/soundfile_backend.py @@ -1,9 +1,20 @@ import os -from typing import Any, Optional, Tuple, Union +from typing import Optional, Tuple import torch from torch import Tensor +from torchaudio._internal import ( + module_utils as _mod_utils, + misc_ops as _misc_ops, +) +from . import common +from .common import SignalInfo, EncodingInfo + +if _mod_utils.is_module_available('soundfile'): + import soundfile + + _subtype_to_precision = { 'PCM_S8': 8, 'PCM_16': 16, @@ -13,43 +24,8 @@ } -class SignalInfo: - def __init__(self, - channels: Optional[int] = None, - rate: Optional[float] = None, - precision: Optional[int] = None, - length: Optional[int] = None) -> None: - self.channels = channels - self.rate = rate - self.precision = precision - self.length = length - - -class EncodingInfo: - def __init__(self, - encoding: Any = None, - bits_per_sample: Optional[int] = None, - compression: Optional[float] = None, - reverse_bytes: Any = None, - reverse_nibbles: Any = None, - reverse_bits: Any = None, - opposite_endian: Optional[bool] = None) -> None: - self.encoding = encoding - self.bits_per_sample = bits_per_sample - self.compression = compression - self.reverse_bytes = reverse_bytes - self.reverse_nibbles = reverse_nibbles - self.reverse_bits = reverse_bits - self.opposite_endian = opposite_endian - - -def check_input(src: Tensor) -> None: - if not torch.is_tensor(src): - raise TypeError("Expected a tensor, got %s" % type(src)) - if src.is_cuda: - raise TypeError("Expected a CPU based tensor, got %s" % type(src)) - - +@_mod_utils.requires_module('soundfile') +@common._impl_load def load(filepath: str, out: Optional[Tensor] = None, normalization: Optional[bool] = True, @@ -80,8 +56,6 @@ def load(filepath: str, if offset < 0: raise ValueError("Expected positive offset value") - import soundfile - # initialize output tensor # TODO call libsoundfile directly to avoid numpy out, sample_rate = soundfile.read( @@ -98,6 +72,15 @@ def load(filepath: str, return out, sample_rate +@_mod_utils.requires_module('soundfile') +@common._impl_load_wav +def load_wav(filepath, **kwargs): + kwargs['normalization'] = 1 << 16 + return load(filepath, **kwargs) + + +@_mod_utils.requires_module('soundfile') +@common._impl_save def save(filepath: str, src: Tensor, sample_rate: int, precision: int = 16, channels_first: bool = True) -> None: r"""See torchaudio.save""" @@ -108,7 +91,7 @@ def save(filepath: str, src: Tensor, sample_rate: int, precision: int = 16, chan if not os.path.isdir(abs_dirpath): raise OSError("Directory does not exist: {}".format(abs_dirpath)) # check that src is a CPU tensor - check_input(src) + _misc_ops.check_input(src) # Check/Fix shape of source data if src.dim() == 1: # 1d tensors as assumed to be mono signals @@ -127,14 +110,14 @@ def save(filepath: str, src: Tensor, sample_rate: int, precision: int = 16, chan precision = "PCM_S8" if precision == 8 else "PCM_" + str(precision) - import soundfile return soundfile.write(filepath, src, sample_rate, precision) +@_mod_utils.requires_module('soundfile') +@common._impl_info def info(filepath: str) -> Tuple[SignalInfo, EncodingInfo]: r"""See torchaudio.info""" - import soundfile sfi = soundfile.info(filepath) precision = _subtype_to_precision[sfi.subtype] diff --git a/torchaudio/backend/sox_backend.py b/torchaudio/backend/sox_backend.py new file mode 100644 index 0000000000..4e2b118f8b --- /dev/null +++ b/torchaudio/backend/sox_backend.py @@ -0,0 +1,275 @@ +import os.path +from typing import Any, Optional, Tuple + +import torch +from torch import Tensor + +from torchaudio._internal import ( + module_utils as _mod_utils, + misc_ops as _misc_ops, +) +from . import common +from .common import SignalInfo, EncodingInfo + +if _mod_utils.is_module_available('torchaudio._torchaudio'): + from torchaudio import _torchaudio + + +@_mod_utils.requires_module('torchaudio._torchaudio') +@common._impl_load +def load(filepath: str, + out: Optional[Tensor] = None, + normalization: bool = True, + channels_first: bool = True, + num_frames: int = 0, + offset: int = 0, + signalinfo: SignalInfo = None, + encodinginfo: EncodingInfo = None, + filetype: Optional[str] = None) -> Tuple[Tensor, int]: + r"""See torchaudio.load""" + + # stringify if `pathlib.Path` (noop if already `str`) + filepath = str(filepath) + # check if valid file + if not os.path.isfile(filepath): + raise OSError("{} not found or is a directory".format(filepath)) + + # initialize output tensor + if out is not None: + _misc_ops.check_input(out) + else: + out = torch.FloatTensor() + + if num_frames < -1: + raise ValueError("Expected value for num_samples -1 (entire file) or >=0") + if offset < 0: + raise ValueError("Expected positive offset value") + + sample_rate = _torchaudio.read_audio_file( + filepath, + out, + channels_first, + num_frames, + offset, + signalinfo, + encodinginfo, + filetype + ) + + # normalize if needed + _misc_ops.normalize_audio(out, normalization) + + return out, sample_rate + + +@_mod_utils.requires_module('torchaudio._torchaudio') +@common._impl_load_wav +def load_wav(filepath, **kwargs): + kwargs['normalization'] = 1 << 16 + return load(filepath, **kwargs) + + +@_mod_utils.requires_module('torchaudio._torchaudio') +@common._impl_save +def save(filepath: str, src: Tensor, sample_rate: int, precision: int = 16, channels_first: bool = True) -> None: + r"""See torchaudio.save""" + + si = sox_signalinfo_t() + ch_idx = 0 if channels_first else 1 + si.rate = sample_rate + si.channels = 1 if src.dim() == 1 else src.size(ch_idx) + si.length = src.numel() + si.precision = precision + return save_encinfo(filepath, src, channels_first, si) + + +@_mod_utils.requires_module('torchaudio._torchaudio') +@common._impl_info +def info(filepath: str) -> Tuple[SignalInfo, EncodingInfo]: + r"""See torchaudio.info""" + return _torchaudio.get_info(filepath) + + +@_mod_utils.requires_module('torchaudio._torchaudio') +def save_encinfo(filepath: str, + src: Tensor, + channels_first: bool = True, + signalinfo: Optional[SignalInfo] = None, + encodinginfo: Optional[EncodingInfo] = None, + filetype: Optional[str] = None) -> None: + r"""Saves a tensor of an audio signal to disk as a standard format like mp3, wav, etc. + + Args: + filepath (str): Path to audio file + src (Tensor): An input 2D tensor of shape `[C x L]` or `[L x C]` where L is + the number of audio frames, C is the number of channels + channels_first (bool, optional): Set channels first or length first in result. (Default: ``True``) + signalinfo (sox_signalinfo_t, optional): A sox_signalinfo_t type, which could be helpful if the + audio type cannot be automatically determined (Default: ``None``). + encodinginfo (sox_encodinginfo_t, optional): A sox_encodinginfo_t type, which could be set if the + audio type cannot be automatically determined (Default: ``None``). + filetype (str, optional): A filetype or extension to be set if sox cannot determine it + automatically. (Default: ``None``) + + Example + >>> data, sample_rate = torchaudio.load('foo.mp3') + >>> torchaudio.save('foo.wav', data, sample_rate) + + """ + ch_idx, len_idx = (0, 1) if channels_first else (1, 0) + + # check if save directory exists + abs_dirpath = os.path.dirname(os.path.abspath(filepath)) + if not os.path.isdir(abs_dirpath): + raise OSError("Directory does not exist: {}".format(abs_dirpath)) + # check that src is a CPU tensor + _misc_ops.check_input(src) + # Check/Fix shape of source data + if src.dim() == 1: + # 1d tensors as assumed to be mono signals + src.unsqueeze_(ch_idx) + elif src.dim() > 2 or src.size(ch_idx) > 16: + # assumes num_channels < 16 + raise ValueError( + "Expected format where C < 16, but found {}".format(src.size())) + # sox stores the sample rate as a float, though practically sample rates are almost always integers + # convert integers to floats + if signalinfo: + if signalinfo.rate and not isinstance(signalinfo.rate, float): + if float(signalinfo.rate) == signalinfo.rate: + signalinfo.rate = float(signalinfo.rate) + else: + raise TypeError('Sample rate should be a float or int') + # check if the bit precision (i.e. bits per sample) is an integer + if signalinfo.precision and not isinstance(signalinfo.precision, int): + if int(signalinfo.precision) == signalinfo.precision: + signalinfo.precision = int(signalinfo.precision) + else: + raise TypeError('Bit precision should be an integer') + # programs such as librosa normalize the signal, unnormalize if detected + if src.min() >= -1.0 and src.max() <= 1.0: + src = src * (1 << 31) + src = src.long() + # set filetype and allow for files with no extensions + extension = os.path.splitext(filepath)[1] + filetype = extension[1:] if len(extension) > 0 else filetype + # transpose from C x L -> L x C + if channels_first: + src = src.transpose(1, 0) + # save data to file + src = src.contiguous() + _torchaudio.write_audio_file(filepath, src, signalinfo, encodinginfo, filetype) + + +@_mod_utils.requires_module('torchaudio._torchaudio') +def sox_signalinfo_t() -> SignalInfo: + r"""Create a sox_signalinfo_t object. This object can be used to set the sample + rate, number of channels, length, bit precision and headroom multiplier + primarily for effects + + Returns: sox_signalinfo_t(object) + - rate (float), sample rate as a float, practically will likely be an integer float + - channel (int), number of audio channels + - precision (int), bit precision + - length (int), length of audio in samples * channels, 0 for unspecified and -1 for unknown + - mult (float, optional), headroom multiplier for effects and ``None`` for no multiplier + + Example + >>> si = torchaudio.sox_signalinfo_t() + >>> si.channels = 1 + >>> si.rate = 16000. + >>> si.precision = 16 + >>> si.length = 0 + """ + return _torchaudio.sox_signalinfo_t() + + +@_mod_utils.requires_module('torchaudio._torchaudio') +def sox_encodinginfo_t() -> EncodingInfo: + r"""Create a sox_encodinginfo_t object. This object can be used to set the encoding + type, bit precision, compression factor, reverse bytes, reverse nibbles, + reverse bits and endianness. This can be used in an effects chain to encode the + final output or to save a file with a specific encoding. For example, one could + use the sox ulaw encoding to do 8-bit ulaw encoding. Note in a tensor output + the result will be a 32-bit number, but number of unique values will be determined by + the bit precision. + + Returns: sox_encodinginfo_t(object) + - encoding (sox_encoding_t), output encoding + - bits_per_sample (int), bit precision, same as `precision` in sox_signalinfo_t + - compression (float), compression for lossy formats, 0.0 for default compression + - reverse_bytes (sox_option_t), reverse bytes, use sox_option_default + - reverse_nibbles (sox_option_t), reverse nibbles, use sox_option_default + - reverse_bits (sox_option_t), reverse bytes, use sox_option_default + - opposite_endian (sox_bool), change endianness, use sox_false + + Example + >>> ei = torchaudio.sox_encodinginfo_t() + >>> ei.encoding = torchaudio.get_sox_encoding_t(1) + >>> ei.bits_per_sample = 16 + >>> ei.compression = 0 + >>> ei.reverse_bytes = torchaudio.get_sox_option_t(2) + >>> ei.reverse_nibbles = torchaudio.get_sox_option_t(2) + >>> ei.reverse_bits = torchaudio.get_sox_option_t(2) + >>> ei.opposite_endian = torchaudio.get_sox_bool(0) + + """ + ei = _torchaudio.sox_encodinginfo_t() + sdo = get_sox_option_t(2) # sox_default_option + ei.reverse_bytes = sdo + ei.reverse_nibbles = sdo + ei.reverse_bits = sdo + return ei + + +@_mod_utils.requires_module('torchaudio._torchaudio') +def get_sox_encoding_t(i: int = None) -> EncodingInfo: + r"""Get enum of sox_encoding_t for sox encodings. + + Args: + i (int, optional): Choose type or get a dict with all possible options + use ``__members__`` to see all options when not specified. (Default: ``None``) + + Returns: + sox_encoding_t: A sox_encoding_t type for output encoding + """ + if i is None: + # one can see all possible values using the .__members__ attribute + return _torchaudio.sox_encoding_t + else: + return _torchaudio.sox_encoding_t(i) + + +@_mod_utils.requires_module('torchaudio._torchaudio') +def get_sox_option_t(i: int = 2) -> Any: + r"""Get enum of sox_option_t for sox encodinginfo options. + + Args: + i (int, optional): Choose type or get a dict with all possible options + use ``__members__`` to see all options when not specified. + (Default: ``sox_option_default`` or ``2``) + Returns: + sox_option_t: A sox_option_t type + """ + if i is None: + return _torchaudio.sox_option_t + else: + return _torchaudio.sox_option_t(i) + + +@_mod_utils.requires_module('torchaudio._torchaudio') +def get_sox_bool(i: int = 0) -> Any: + r"""Get enum of sox_bool for sox encodinginfo options. + + Args: + i (int, optional): Choose type or get a dict with all possible options + use ``__members__`` to see all options when not specified. (Default: + ``sox_false`` or ``0``) + + Returns: + sox_bool: A sox_bool type + """ + if i is None: + return _torchaudio.sox_bool + else: + return _torchaudio.sox_bool(i) diff --git a/torchaudio/backend/utils.py b/torchaudio/backend/utils.py new file mode 100644 index 0000000000..d537f01daf --- /dev/null +++ b/torchaudio/backend/utils.py @@ -0,0 +1,74 @@ +"""Defines utilities for switching audio backends""" +import warnings +from typing import Optional, List + +import torchaudio +from torchaudio._internal.module_utils import is_module_available +from . import ( + no_backend, + sox_backend, + soundfile_backend, +) + +__all__ = [ + 'list_audio_backends', + 'get_audio_backend', + 'set_audio_backend', +] + + +def list_audio_backends() -> List[str]: + """List available backends""" + backends = [] + if is_module_available('soundfile'): + backends.append('soundfile') + if is_module_available('torchaudio._torchaudio'): + backends.append('sox') + return backends + + +def set_audio_backend(backend: Optional[str]) -> None: + """Set the backend for I/O operation + + Args: + backend (str): Name of the backend. One of "sox" or "soundfile", + based on availability of the system. + """ + if backend is not None and backend not in list_audio_backends(): + raise RuntimeError( + f'Backend "{backend}" is not one of ' + f'available backends: {list_audio_backends()}.') + + if backend is None: + module = no_backend + elif backend == 'sox': + module = sox_backend + elif backend == 'soundfile': + module = soundfile_backend + else: + raise NotImplementedError(f'Unexpected backend "{backend}"') + + for func in ['save', 'load', 'load_wav', 'info']: + setattr(torchaudio, func, getattr(module, func)) + + +def _init_audio_backend(): + backends = list_audio_backends() + if 'sox' in backends: + set_audio_backend('sox') + elif 'soundfile' in backends: + set_audio_backend('soundfile') + else: + warnings.warn('No audio backend is available.') + set_audio_backend(None) + + +def get_audio_backend() -> Optional[str]: + """Get the name of the current backend""" + if torchaudio.load == no_backend.load: + return None + if torchaudio.load == sox_backend.load: + return 'sox' + if torchaudio.load == soundfile_backend.load: + return 'soundfile' + raise ValueError('Unknown backend.') diff --git a/torchaudio/common_utils.py b/torchaudio/common_utils.py deleted file mode 100644 index 28b77200a6..0000000000 --- a/torchaudio/common_utils.py +++ /dev/null @@ -1,38 +0,0 @@ -import sys - -PY3 = sys.version_info > (3, 0) -PY34 = sys.version_info >= (3, 4) - - -def _check_module_exists(name: str) -> bool: - r"""Returns if a top-level module with :attr:`name` exists *without** - importing it. This is generally safer than try-catch block around a - `import X`. It avoids third party libraries breaking assumptions of some of - our tests, e.g., setting multiprocessing start method when imported - (see librosa/#747, torchvision/#544). - """ - if not PY3: # Python 2 - import imp - try: - imp.find_module(name) - return True - except ImportError: - return False - elif not PY34: # Python [3, 3.4) - import importlib - loader = importlib.find_loader(name) - return loader is not None - else: # Python >= 3.4 - import importlib - import importlib.util - spec = importlib.util.find_spec(name) - return spec is not None - -IMPORT_NUMPY = _check_module_exists('numpy') -IMPORT_KALDI_IO = _check_module_exists('kaldi_io') -IMPORT_SCIPY = _check_module_exists('scipy') - -# On Py2, importing librosa 0.6.1 triggers a TypeError (if using newest joblib) -# see librosa/librosa#729. -# TODO: allow Py2 when librosa 0.6.2 releases -IMPORT_LIBROSA = _check_module_exists('librosa') and PY3 diff --git a/torchaudio/compliance/kaldi.py b/torchaudio/compliance/kaldi.py index bdf5f407ba..6211b09f2d 100644 --- a/torchaudio/compliance/kaldi.py +++ b/torchaudio/compliance/kaldi.py @@ -272,6 +272,9 @@ def spectrogram(waveform: Tensor, Tensor: A spectrogram identical to what Kaldi would output. The shape is (m, ``padded_window_size // 2 + 1``) where m is calculated in _get_strided """ + device, dtype = waveform.device, waveform.dtype + epsilon = _get_epsilon(device, dtype) + waveform, window_shift, window_size, padded_window_size = _get_waveform_and_window_properties( waveform, channel, sample_frequency, frame_shift, frame_length, round_to_power_of_two, preemphasis_coefficient) @@ -287,7 +290,7 @@ def spectrogram(waveform: Tensor, fft = torch.rfft(strided_input, 1, normalized=False, onesided=True) # Convert the FFT into a power spectrum - power_spectrum = torch.max(fft.pow(2).sum(2), EPSILON).log() # size (m, padded_window_size // 2 + 1) + power_spectrum = torch.max(fft.pow(2).sum(2), epsilon).log() # size (m, padded_window_size // 2 + 1) power_spectrum[:, 0] = signal_log_energy power_spectrum = _subtract_column_mean(power_spectrum, subtract_mean) diff --git a/torchaudio/csrc/register.cpp b/torchaudio/csrc/register.cpp new file mode 100644 index 0000000000..81b1a84c96 --- /dev/null +++ b/torchaudio/csrc/register.cpp @@ -0,0 +1,18 @@ +#ifndef TORCHAUDIO_REGISTER_H +#define TORCHAUDIO_REGISTER_H + +#include + +namespace torchaudio { +namespace { + +static auto registerSignalInfo = + torch::class_("torchaudio", "SignalInfo") + .def(torch::init()) + .def("get_sample_rate", &SignalInfo::getSampleRate) + .def("get_num_channels", &SignalInfo::getNumChannels) + .def("get_num_samples", &SignalInfo::getNumSamples); + +} // namespace +} // namespace torchaudio +#endif diff --git a/torchaudio/csrc/typedefs.cpp b/torchaudio/csrc/typedefs.cpp new file mode 100644 index 0000000000..7b81d665dc --- /dev/null +++ b/torchaudio/csrc/typedefs.cpp @@ -0,0 +1,23 @@ +#include + +namespace torchaudio { +SignalInfo::SignalInfo( + const int64_t sample_rate_, + const int64_t num_channels_, + const int64_t num_samples_) + : sample_rate(sample_rate_), + num_channels(num_channels_), + num_samples(num_samples_){}; + +int64_t SignalInfo::getSampleRate() const { + return sample_rate; +} + +int64_t SignalInfo::getNumChannels() const { + return num_channels; +} + +int64_t SignalInfo::getNumSamples() const { + return num_samples; +} +} // namespace torchaudio diff --git a/torchaudio/csrc/typedefs.h b/torchaudio/csrc/typedefs.h new file mode 100644 index 0000000000..646ed09f3d --- /dev/null +++ b/torchaudio/csrc/typedefs.h @@ -0,0 +1,23 @@ +#ifndef TORCHAUDIO_TYPDEFS_H +#define TORCHAUDIO_TYPDEFS_H + +#include + +namespace torchaudio { +struct SignalInfo : torch::CustomClassHolder { + int64_t sample_rate; + int64_t num_channels; + int64_t num_samples; + + SignalInfo( + const int64_t sample_rate_, + const int64_t num_channels_, + const int64_t num_samples_); + int64_t getSampleRate() const; + int64_t getNumChannels() const; + int64_t getNumSamples() const; +}; + +} // namespace torchaudio + +#endif diff --git a/torchaudio/datasets/__init__.py b/torchaudio/datasets/__init__.py index 1741889029..942f2c83a1 100644 --- a/torchaudio/datasets/__init__.py +++ b/torchaudio/datasets/__init__.py @@ -6,6 +6,7 @@ from .gtzan import GTZAN from .yesno import YESNO from .ljspeech import LJSPEECH +from .cmuarctic import CMUARCTIC __all__ = ( "COMMONVOICE", @@ -15,6 +16,7 @@ "YESNO", "LJSPEECH", "GTZAN", + "CMUARCTIC", "diskcache_iterator", "bg_iterator", ) diff --git a/torchaudio/datasets/cmuarctic.py b/torchaudio/datasets/cmuarctic.py new file mode 100644 index 0000000000..7aa6d541e5 --- /dev/null +++ b/torchaudio/datasets/cmuarctic.py @@ -0,0 +1,150 @@ +import os +from typing import Tuple + +import torchaudio +from torch import Tensor +from torch.utils.data import Dataset +from torchaudio.datasets.utils import ( + download_url, + extract_archive, + unicode_csv_reader, +) + +URL = "aew" +FOLDER_IN_ARCHIVE = "ARCTIC" +_CHECKSUMS = { + "http://festvox.org/cmu_arctic/packed/cmu_us_aew_arctic.tar.bz2": + "4382b116efcc8339c37e01253cb56295", + "http://festvox.org/cmu_arctic/packed/cmu_us_ahw_arctic.tar.bz2": + "b072d6e961e3f36a2473042d097d6da9", + "http://festvox.org/cmu_arctic/packed/cmu_us_aup_arctic.tar.bz2": + "5301c7aee8919d2abd632e2667adfa7f", + "http://festvox.org/cmu_arctic/packed/cmu_us_awb_arctic.tar.bz2": + "280fdff1e9857119d9a2c57b50e12db7", + "http://festvox.org/cmu_arctic/packed/cmu_us_axb_arctic.tar.bz2": + "5e21cb26c6529c533df1d02ccde5a186", + "http://festvox.org/cmu_arctic/packed/cmu_us_bdl_arctic.tar.bz2": + "b2c3e558f656af2e0a65da0ac0c3377a", + "http://festvox.org/cmu_arctic/packed/cmu_us_clb_arctic.tar.bz2": + "3957c503748e3ce17a3b73c1b9861fb0", + "http://festvox.org/cmu_arctic/packed/cmu_us_eey_arctic.tar.bz2": + "59708e932d27664f9eda3e8e6859969b", + "http://festvox.org/cmu_arctic/packed/cmu_us_fem_arctic.tar.bz2": + "dba4f992ff023347c07c304bf72f4c73", + "http://festvox.org/cmu_arctic/packed/cmu_us_gka_arctic.tar.bz2": + "24a876ea7335c1b0ff21460e1241340f", + "http://festvox.org/cmu_arctic/packed/cmu_us_jmk_arctic.tar.bz2": + "afb69d95f02350537e8a28df5ab6004b", + "http://festvox.org/cmu_arctic/packed/cmu_us_ksp_arctic.tar.bz2": + "4ce5b3b91a0a54b6b685b1b05aa0b3be", + "http://festvox.org/cmu_arctic/packed/cmu_us_ljm_arctic.tar.bz2": + "6f45a3b2c86a4ed0465b353be291f77d", + "http://festvox.org/cmu_arctic/packed/cmu_us_lnh_arctic.tar.bz2": + "c6a15abad5c14d27f4ee856502f0232f", + "http://festvox.org/cmu_arctic/packed/cmu_us_rms_arctic.tar.bz2": + "71072c983df1e590d9e9519e2a621f6e", + "http://festvox.org/cmu_arctic/packed/cmu_us_rxr_arctic.tar.bz2": + "3771ff03a2f5b5c3b53aa0a68b9ad0d5", + "http://festvox.org/cmu_arctic/packed/cmu_us_slp_arctic.tar.bz2": + "9cbf984a832ea01b5058ba9a96862850", + "http://festvox.org/cmu_arctic/packed/cmu_us_slt_arctic.tar.bz2": + "959eecb2cbbc4ac304c6b92269380c81", +} + + +def load_cmuarctic_item(line: str, + path: str, + folder_audio: str, + ext_audio: str) -> Tuple[Tensor, int, str, str]: + + utterance_id, utterance = line[0].strip().split(" ", 2)[1:] + + # Remove space, double quote, and single parenthesis from utterance + utterance = utterance[1:-3] + + file_audio = os.path.join(path, folder_audio, utterance_id + ext_audio) + + # Load audio + waveform, sample_rate = torchaudio.load(file_audio) + + return ( + waveform, + sample_rate, + utterance, + utterance_id.split("_")[1] + ) + + +class CMUARCTIC(Dataset): + """ + Create a Dataset for CMU_arctic. Each item is a tuple of the form: + waveform, sample_rate, utterance, utterance_id + """ + + _file_text = "txt.done.data" + _folder_text = "etc" + _ext_audio = ".wav" + _folder_audio = "wav" + + def __init__(self, + root: str, + url: str = URL, + folder_in_archive: str = FOLDER_IN_ARCHIVE, + download: bool = False) -> None: + + if url in [ + "aew", + "ahw", + "aup", + "awb", + "axb", + "bdl", + "clb", + "eey", + "fem", + "gka", + "jmk", + "ksp", + "ljm", + "lnh", + "rms", + "rxr", + "slp", + "slt" + ]: + + url = "cmu_us_" + url + "_arctic" + ext_archive = ".tar.bz2" + base_url = "http://www.festvox.org/cmu_arctic/packed/" + + url = os.path.join(base_url, url + ext_archive) + + basename = os.path.basename(url) + root = os.path.join(root, folder_in_archive) + if not os.path.isdir(root): + os.mkdir(root) + archive = os.path.join(root, basename) + + basename = basename.split(".")[0] + + self._path = os.path.join(root, basename) + + if download: + if not os.path.isdir(self._path): + if not os.path.isfile(archive): + checksum = _CHECKSUMS.get(url, None) + download_url(url, root, hash_value=checksum, hash_type="md5") + extract_archive(archive) + + self._text = os.path.join(self._path, self._folder_text, self._file_text) + + with open(self._text, "r") as text: + walker = unicode_csv_reader(text, delimiter="\n") + self._walker = list(walker) + + def __getitem__(self, n: int) -> Tuple[Tensor, int, str, str]: + line = self._walker[n] + return load_cmuarctic_item(line, self._path, self._folder_audio, self._ext_audio) + + def __len__(self) -> int: + return len(self._walker) diff --git a/torchaudio/extension/__init__.py b/torchaudio/extension/__init__.py new file mode 100644 index 0000000000..d9b6c76fac --- /dev/null +++ b/torchaudio/extension/__init__.py @@ -0,0 +1,7 @@ +from .extension import ( + _init_extension, +) + +_init_extension() + +del _init_extension diff --git a/torchaudio/extension/extension.py b/torchaudio/extension/extension.py new file mode 100644 index 0000000000..4a2ab82124 --- /dev/null +++ b/torchaudio/extension/extension.py @@ -0,0 +1,49 @@ +import warnings +import importlib +from collections import namedtuple + +import torch +from torchaudio._internal import module_utils as _mod_utils + + +def _init_extension(): + ext = 'torchaudio._torchaudio' + if _mod_utils.is_module_available(ext): + _init_script_module(ext) + else: + warnings.warn('torchaudio C++ extension is not available.') + _init_dummy_module() + + +def _init_script_module(module): + path = importlib.util.find_spec(module).origin + torch.classes.load_library(path) + torch.ops.load_library(path) + + +def _init_dummy_module(): + class SignalInfo: + """Data class for audio format information + + Used when torchaudio C++ extension is not available for annotating + sox_io backend functions so that torchaudio is still importable + without extension. + This class has to implement the same interface as C++ equivalent. + """ + def __init__(self, sample_rate: int, num_channels: int, num_samples: int): + self.sample_rate = sample_rate + self.num_channels = num_channels + self.num_samples = num_samples + + def get_sample_rate(self): + return self.sample_rate + + def get_num_channels(self): + return self.num_channels + + def get_num_samples(self): + return self.num_samples + + DummyModule = namedtuple('torchaudio', ['SignalInfo']) + module = DummyModule(SignalInfo) + setattr(torch.classes, 'torchaudio', module) diff --git a/torchaudio/functional.py b/torchaudio/functional.py index 92580e1d9e..28cb6a3fa2 100644 --- a/torchaudio/functional.py +++ b/torchaudio/functional.py @@ -265,7 +265,7 @@ def griffinlim( angles = rebuilt if momentum: angles = angles - tprev.mul_(momentum / (1 + momentum)) - angles = angles.div_(complex_norm(angles).add_(1e-16).unsqueeze(-1).expand_as(angles)) + angles = angles.div(complex_norm(angles).add(1e-16).unsqueeze(-1).expand_as(angles)) # Return the final phase estimates waveform = istft(specgram * angles, @@ -1317,7 +1317,6 @@ def phaser( delay_buf = torch.zeros(waveform.shape[0], delay_buf_len, dtype=dtype, device=device) mod_buf_len = int(sample_rate / mod_speed + .5) - mod_buf = torch.zeros(mod_buf_len, dtype=dtype, device=device) if sinusoidal: wave_type = 'SINE' @@ -1329,7 +1328,8 @@ def phaser( table_size=mod_buf_len, min=1., max=float(delay_buf_len), - phase=math.pi / 2) + phase=math.pi / 2, + device=device) delay_pos = 0 mod_pos = 0 @@ -1353,7 +1353,8 @@ def _generate_wave_table( table_size: int, min: float, max: float, - phase: float + phase: float, + device: torch.device ) -> Tensor: r"""A helper fucntion for phaser. Generates a table with given parameters @@ -1364,24 +1365,24 @@ def _generate_wave_table( min (float): desired min value max (float): desired max value phase (float): desired phase - + device (torch.device): Torch device on which table must be generated Returns: Tensor: A 1D tensor with wave table values """ phase_offset = int(phase / math.pi / 2 * table_size + 0.5) - t = torch.arange(table_size).to(torch.int32) + t = torch.arange(table_size, device=device, dtype=torch.int32) point = (t + phase_offset) % table_size - d = torch.zeros_like(point).to(torch.float64) + d = torch.zeros_like(point, device=device, dtype=torch.float64) if wave_type == 'SINE': d = (torch.sin(point.to(torch.float64) / table_size * 2 * math.pi) + 1) / 2 elif wave_type == 'TRIANGLE': d = point.to(torch.float64) * 2 / table_size - value = 4 * point / table_size + value = 4 * point // table_size d[value == 0] = d[value == 0] + 0.5 d[value == 1] = 1.5 - d[value == 1] d[value == 2] = 1.5 - d[value == 2] @@ -1487,8 +1488,6 @@ def flanger( lfo_length = int(sample_rate / speed) - lfo = torch.zeros(lfo_length, dtype=dtype, device=device) - table_min = math.floor(delay_min * sample_rate + 0.5) table_max = delay_buf_length - 2. @@ -1497,13 +1496,14 @@ def flanger( table_size=lfo_length, min=float(table_min), max=float(table_max), - phase=3 * math.pi / 2) + phase=3 * math.pi / 2, + device=device) output_waveform = torch.zeros_like(waveform, dtype=dtype, device=device) delay_buf_pos = 0 lfo_pos = 0 - channel_idxs = torch.arange(0, n_channels) + channel_idxs = torch.arange(0, n_channels, device=device) for i in range(waveform.shape[-1]): diff --git a/torchaudio/kaldi_io.py b/torchaudio/kaldi_io.py index d32aaed357..7f14f48e2c 100644 --- a/torchaudio/kaldi_io.py +++ b/torchaudio/kaldi_io.py @@ -1,16 +1,14 @@ # To use this file, the dependency (https://github.com/vesis84/kaldi-io-for-python) # needs to be installed. This is a light wrapper around kaldi_io that returns # torch.Tensors. -from typing import Any, Callable, Iterable, Tuple, Union +from typing import Any, Callable, Iterable, Tuple import torch from torch import Tensor -from torchaudio.common_utils import IMPORT_KALDI_IO, IMPORT_NUMPY +from torchaudio._internal import module_utils as _mod_utils -if IMPORT_NUMPY: +if _mod_utils.is_module_available('kaldi_io', 'numpy'): import numpy as np - -if IMPORT_KALDI_IO: import kaldi_io @@ -38,15 +36,13 @@ def _convert_method_output_to_tensor(file_or_fd: Any, Returns: Iterable[Tuple[str, Tensor]]: The string is the key and the tensor is vec/mat """ - if not IMPORT_KALDI_IO: - raise ImportError('Could not import kaldi_io. Did you install it?') - for key, np_arr in fn(file_or_fd): if convert_contiguous: np_arr = np.ascontiguousarray(np_arr) yield key, torch.from_numpy(np_arr) +@_mod_utils.requires_module('kaldi_io', 'numpy') def read_vec_int_ark(file_or_fd: Any) -> Iterable[Tuple[str, Tensor]]: r"""Create generator of (key,vector) tuples, which reads from the ark file/stream. @@ -66,6 +62,7 @@ def read_vec_int_ark(file_or_fd: Any) -> Iterable[Tuple[str, Tensor]]: return _convert_method_output_to_tensor(file_or_fd, kaldi_io.read_vec_int_ark, convert_contiguous=True) +@_mod_utils.requires_module('kaldi_io', 'numpy') def read_vec_flt_scp(file_or_fd: Any) -> Iterable[Tuple[str, Tensor]]: r"""Create generator of (key,vector) tuples, read according to Kaldi scp. @@ -82,6 +79,7 @@ def read_vec_flt_scp(file_or_fd: Any) -> Iterable[Tuple[str, Tensor]]: return _convert_method_output_to_tensor(file_or_fd, kaldi_io.read_vec_flt_scp) +@_mod_utils.requires_module('kaldi_io', 'numpy') def read_vec_flt_ark(file_or_fd: Any) -> Iterable[Tuple[str, Tensor]]: r"""Create generator of (key,vector) tuples, which reads from the ark file/stream. @@ -98,6 +96,7 @@ def read_vec_flt_ark(file_or_fd: Any) -> Iterable[Tuple[str, Tensor]]: return _convert_method_output_to_tensor(file_or_fd, kaldi_io.read_vec_flt_ark) +@_mod_utils.requires_module('kaldi_io', 'numpy') def read_mat_scp(file_or_fd: Any) -> Iterable[Tuple[str, Tensor]]: r"""Create generator of (key,matrix) tuples, read according to Kaldi scp. @@ -114,6 +113,7 @@ def read_mat_scp(file_or_fd: Any) -> Iterable[Tuple[str, Tensor]]: return _convert_method_output_to_tensor(file_or_fd, kaldi_io.read_mat_scp) +@_mod_utils.requires_module('kaldi_io', 'numpy') def read_mat_ark(file_or_fd: Any) -> Iterable[Tuple[str, Tensor]]: r"""Create generator of (key,matrix) tuples, which reads from the ark file/stream. diff --git a/torchaudio/models/__init__.py b/torchaudio/models/__init__.py index 1abdac6271..20c1bdf265 100644 --- a/torchaudio/models/__init__.py +++ b/torchaudio/models/__init__.py @@ -1 +1,2 @@ from .wav2letter import * +from ._wavernn import * diff --git a/torchaudio/models/_wavernn.py b/torchaudio/models/_wavernn.py new file mode 100644 index 0000000000..04155fb87c --- /dev/null +++ b/torchaudio/models/_wavernn.py @@ -0,0 +1,105 @@ +from torch import Tensor +from torch import nn + +__all__ = ["_ResBlock", "_MelResNet"] + + +class _ResBlock(nn.Module): + r"""This is a ResNet block layer. This layer is based on the paper "Deep Residual Learning + for Image Recognition". Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. CVPR, 2016. + It is a block used in WaveRNN. WaveRNN is based on the paper "Efficient Neural Audio Synthesis". + Nal Kalchbrenner, Erich Elsen, Karen Simonyan, Seb Noury, Norman Casagrande, Edward Lockhart, + Florian Stimberg, Aaron van den Oord, Sander Dieleman, Koray Kavukcuoglu. arXiv:1802.08435, 2018. + + Args: + num_dims: the number of compute dimensions in the input (default=128). + + Examples:: + >>> resblock = _ResBlock(num_dims=128) + >>> input = torch.rand(10, 128, 512) + >>> output = resblock(input) + """ + + def __init__(self, num_dims: int = 128) -> None: + super().__init__() + + self.resblock_model = nn.Sequential( + nn.Conv1d(in_channels=num_dims, out_channels=num_dims, kernel_size=1, bias=False), + nn.BatchNorm1d(num_dims), + nn.ReLU(inplace=True), + nn.Conv1d(in_channels=num_dims, out_channels=num_dims, kernel_size=1, bias=False), + nn.BatchNorm1d(num_dims) + ) + + def forward(self, x: Tensor) -> Tensor: + r"""Pass the input through the _ResBlock layer. + + Args: + x: the input sequence to the _ResBlock layer (required). + + Shape: + - x: :math:`(N, S, T)`. + - output: :math:`(N, S, T)`. + where N is the batch size, S is the number of input sequence, + T is the length of input sequence. + """ + + residual = x + return self.resblock_model(x) + residual + + +class _MelResNet(nn.Module): + r"""This is a MelResNet layer based on a stack of ResBlocks. It is a block used in WaveRNN. + WaveRNN is based on the paper "Efficient Neural Audio Synthesis". Nal Kalchbrenner, Erich Elsen, + Karen Simonyan, Seb Noury, Norman Casagrande, Edward Lockhart, Florian Stimberg, Aaron van den Oord, + Sander Dieleman, Koray Kavukcuoglu. arXiv:1802.08435, 2018. + + Args: + res_blocks: the number of ResBlock in stack (default=10). + input_dims: the number of input sequence (default=100). + hidden_dims: the number of compute dimensions (default=128). + output_dims: the number of output sequence (default=128). + pad: the number of kernal size (pad * 2 + 1) in the first Conv1d layer (default=2). + + Examples:: + >>> melresnet = _MelResNet(res_blocks=10, input_dims=100, + hidden_dims=128, output_dims=128, pad=2) + >>> input = torch.rand(10, 100, 512) + >>> output = melresnet(input) + """ + + def __init__(self, res_blocks: int = 10, + input_dims: int = 100, + hidden_dims: int = 128, + output_dims: int = 128, + pad: int = 2) -> None: + super().__init__() + + kernel_size = pad * 2 + 1 + ResBlocks = [] + + for i in range(res_blocks): + ResBlocks.append(_ResBlock(hidden_dims)) + + self.melresnet_model = nn.Sequential( + nn.Conv1d(in_channels=input_dims, out_channels=hidden_dims, kernel_size=kernel_size, bias=False), + nn.BatchNorm1d(hidden_dims), + nn.ReLU(inplace=True), + *ResBlocks, + nn.Conv1d(in_channels=hidden_dims, out_channels=output_dims, kernel_size=1) + ) + + def forward(self, x: Tensor) -> Tensor: + r"""Pass the input through the _MelResNet layer. + + Args: + x: the input sequence to the _MelResNet layer (required). + + Shape: + - x: :math:`(N, S, T)`. + - output: :math:`(N, P, T - 2 * pad)`. + where N is the batch size, S is the number of input sequence, + P is the number of output sequence, T is the length of input sequence. + """ + + return self.melresnet_model(x) diff --git a/torchaudio/sox_effects/__init__.py b/torchaudio/sox_effects/__init__.py new file mode 100644 index 0000000000..115b70c895 --- /dev/null +++ b/torchaudio/sox_effects/__init__.py @@ -0,0 +1,12 @@ +from torchaudio._internal import module_utils as _mod_utils +from .sox_effects import ( + init_sox_effects, + shutdown_sox_effects, + effect_names, + SoxEffect, + SoxEffectsChain, +) + + +if _mod_utils.is_module_available('torchaudio._torchaudio'): + init_sox_effects() diff --git a/torchaudio/sox_effects.py b/torchaudio/sox_effects/sox_effects.py similarity index 71% rename from torchaudio/sox_effects.py rename to torchaudio/sox_effects/sox_effects.py index d1eb04e53f..6a6b5be0a5 100644 --- a/torchaudio/sox_effects.py +++ b/torchaudio/sox_effects/sox_effects.py @@ -1,12 +1,85 @@ +import atexit from typing import Any, Callable, List, Optional, Tuple, Union import torch -import torchaudio from torch import Tensor -from torchaudio._backend import _audio_backend_guard +from torchaudio._internal import ( + module_utils as _mod_utils, + misc_ops as _misc_ops, +) -@_audio_backend_guard("sox") +if _mod_utils.is_module_available('torchaudio._torchaudio'): + from torchaudio import _torchaudio + + +_SOX_INITIALIZED: Optional[bool] = False +# This variable has a micro lifecycle. (False -> True -> None) +# False: Not initialized +# True: Initialized +# None: Already shut down (should not be initialized again.) + +_SOX_SUCCESS_CODE = 0 +# defined at +# https://fossies.org/dox/sox-14.4.2/sox_8h.html#a8e07e80cebeff3339265d89c387cea93a9ef2b87ec303edfe40751d9a85fadeeb + + +@_mod_utils.requires_module('torchaudio._torchaudio') +def init_sox_effects() -> int: + """Initialize resources required to use ``SoxEffectsChain`` + + You do not need to call this function manually. It is called automatically. + + Once initialized, you do not need to call this function again across the multiple call of + ``SoxEffectsChain.sox_build_flow_effects``, though it is safe to do so as long as + ``shutdown_sox_effects`` is not called yet. + Once ``shutdown_sox_effects`` is called, you can no longer use SoX effects and calling + this function results in `RuntimeError`. + + Note: + This function is not required for simple loading. + + Returns: + int: Code corresponding to sox_error_t enum. See + https://fossies.org/dox/sox-14.4.2/sox_8h.html#a8e07e80cebeff3339265d89c387cea93 + """ + global _SOX_INITIALIZED + if _SOX_INITIALIZED is None: + raise RuntimeError('SoX effects chain has been already shut down. Can not initialize again.') + if not _SOX_INITIALIZED: + code = _torchaudio.initialize_sox() + if code == _SOX_SUCCESS_CODE: + _SOX_INITIALIZED = True + atexit.register(shutdown_sox_effects) + return code + return _SOX_SUCCESS_CODE + + +@_mod_utils.requires_module("torchaudio._torchaudio") +def shutdown_sox_effects() -> int: + """Clean up resources required to use ``SoxEffectsChain`` + + You do not need to call this function manually. It is called automatically. + + It is safe to call this function multiple times. + Once ``shutdown_sox_effects`` is called, you can no longer use SoX effects and calling + this function results in `RuntimeError`. + + + Returns: + int: Code corresponding to sox_error_t enum. See + https://fossies.org/dox/sox-14.4.2/sox_8h.html#a8e07e80cebeff3339265d89c387cea93 + """ + global _SOX_INITIALIZED + if _SOX_INITIALIZED: + code = _torchaudio.shutdown_sox() + if code == _SOX_INITIALIZED: + _SOX_INITIALIZED = None + return code + return _SOX_SUCCESS_CODE + + +@_mod_utils.requires_module('torchaudio._torchaudio') def effect_names() -> List[str]: """Gets list of valid sox effect names @@ -15,12 +88,10 @@ def effect_names() -> List[str]: Example >>> EFFECT_NAMES = torchaudio.sox_effects.effect_names() """ - - from . import _torchaudio return _torchaudio.get_effect_names() -@_audio_backend_guard("sox") +@_mod_utils.requires_module('torchaudio._torchaudio') def SoxEffect(): r"""Create an object for passing sox effect information between python and c++ @@ -28,8 +99,6 @@ def SoxEffect(): SoxEffect: An object with the following attributes: ename (str) which is the name of effect, and eopts (List[str]) which is a list of effect options. """ - - from . import _torchaudio return _torchaudio.SoxEffect() @@ -123,7 +192,7 @@ def append_effect_to_chain(self, e.eopts = eargs self.chain.append(e) - @_audio_backend_guard("sox") + @_mod_utils.requires_module('torchaudio._torchaudio') def sox_build_flow_effects(self, out: Optional[Tensor] = None) -> Tuple[Tensor, int]: r"""Build effects chain and flow effects from input file to output tensor @@ -138,7 +207,7 @@ def sox_build_flow_effects(self, """ # initialize output tensor if out is not None: - torchaudio.check_input(out) + _misc_ops.check_input(out) else: out = torch.FloatTensor() if not len(self.chain): @@ -149,8 +218,6 @@ def sox_build_flow_effects(self, # print("effect options:", [x.eopts for x in self.chain]) - torchaudio.initialize_sox() - from . import _torchaudio sr = _torchaudio.build_flow_effects(self.input_file, out, self.channels_first, @@ -160,7 +227,7 @@ def sox_build_flow_effects(self, self.chain, self.MAX_EFFECT_OPTS) - torchaudio._audio_normalization(out, self.normalization) + _misc_ops.normalize_audio(out, self.normalization) return out, sr From 36414bcc0d3b6bb575d0b5dda21735ed98317610 Mon Sep 17 00:00:00 2001 From: Moto Hira Date: Fri, 17 Jul 2020 09:28:17 -0700 Subject: [PATCH 10/19] Import torchaudio 20200714 #782 (#784) Summary: Pull Request resolved: https://github.com/pytorch/audio/pull/784 - Import torchaudio. - Change test util module name from test_case_utils to case_utils Reviewed By: cpuhrsch Differential Revision: D22261638 fbshipit-source-id: eb4df500c1d7db0a60baa100dd22795a63851438 --- README.md | 4 +- .../setup_helpers/build_third_party.sh | 50 --- .../setup_helpers/build_third_party_helper.sh | 217 ----------- build_tools/setup_helpers/extension.py | 36 +- packaging/build_conda.sh | 2 +- packaging/build_wheel.sh | 2 +- packaging/pkg_helpers.bash | 3 +- setup.py | 2 +- test/README.md | 52 +++ .../tt/clips/common_voice_tt_00000000.mp3 | Bin 57136 -> 0 bytes .../tt/clips/common_voice_tt_00000000.wav | Bin 0 -> 80700 bytes .../cv-corpus-4-2019-12-10/tt/train.tsv | 4 +- test/assets/dtmf_30s_stereo.mp3 | Bin 228984 -> 0 bytes test/assets/io/96k_0_1ch.opus | Bin 0 -> 8259 bytes test/assets/io/96k_0_2ch.opus | Bin 0 -> 6740 bytes test/assets/io/96k_10_1ch.opus | Bin 0 -> 15033 bytes test/assets/io/96k_10_2ch.opus | Bin 0 -> 15160 bytes test/assets/io/96k_5_1ch.opus | Bin 0 -> 13074 bytes test/assets/io/96k_5_2ch.opus | Bin 0 -> 10618 bytes test/assets/io/generate_opus.py | 50 +++ test/common_utils.py | 171 --------- test/common_utils/__init__.py | 31 ++ test/common_utils/backend_utils.py | 41 ++ test/common_utils/case_utils.py | 75 ++++ test/common_utils/data_utils.py | 94 +++++ test/common_utils/parameterized_utils.py | 10 + test/common_utils/sox_utils.py | 79 ++++ test/common_utils/wav_utils.py | 86 +++++ test/functional_cpu_test.py | 35 +- test/kaldi_compatibility_impl.py | 52 ++- test/sox_io_backend/__init__.py | 0 test/sox_io_backend/common.py | 2 + test/sox_io_backend/test_info.py | 125 +++++++ test/sox_io_backend/test_load.py | 263 +++++++++++++ test/sox_io_backend/test_roundtrip.py | 52 +++ test/sox_io_backend/test_save.py | 304 +++++++++++++++ test/sox_io_backend/test_torchscript.py | 149 ++++++++ test/test_backend.py | 15 +- test/test_datasets.py | 3 +- test/test_io.py | 33 +- test/test_librosa_compatibility.py | 9 +- test/test_models.py | 117 +++++- test/test_sox_compatibility.py | 206 +++++------ test/test_sox_effects.py | 4 +- test/test_transforms.py | 6 +- test/torchscript_consistency_impl.py | 4 +- third_party/CMakeLists.txt | 80 ++++ third_party/build_codec_helper.sh | 13 + third_party/patch/libmad.patch | 86 +++++ torchaudio/backend/sox_io_backend.py | 134 +++++++ torchaudio/backend/utils.py | 6 + torchaudio/csrc/register.cpp | 59 ++- torchaudio/csrc/sox.cpp | 33 -- torchaudio/csrc/sox.h | 7 - torchaudio/csrc/sox_effects.cpp | 54 +++ torchaudio/csrc/sox_effects.h | 18 + torchaudio/csrc/sox_io.cpp | 170 +++++++++ torchaudio/csrc/sox_io.h | 41 ++ torchaudio/csrc/sox_utils.cpp | 245 ++++++++++++ torchaudio/csrc/sox_utils.h | 100 +++++ torchaudio/csrc/typedefs.cpp | 23 -- torchaudio/csrc/typedefs.h | 23 -- torchaudio/extension/extension.py | 29 -- torchaudio/functional.py | 7 +- torchaudio/models/_wavernn.py | 350 ++++++++++++++---- torchaudio/sox_effects/__init__.py | 2 + torchaudio/sox_effects/sox_effects.py | 54 +-- 67 files changed, 3049 insertions(+), 873 deletions(-) delete mode 100755 build_tools/setup_helpers/build_third_party.sh delete mode 100644 build_tools/setup_helpers/build_third_party_helper.sh delete mode 100644 test/assets/CommonVoice/cv-corpus-4-2019-12-10/tt/clips/common_voice_tt_00000000.mp3 create mode 100644 test/assets/CommonVoice/cv-corpus-4-2019-12-10/tt/clips/common_voice_tt_00000000.wav delete mode 100644 test/assets/dtmf_30s_stereo.mp3 create mode 100644 test/assets/io/96k_0_1ch.opus create mode 100644 test/assets/io/96k_0_2ch.opus create mode 100644 test/assets/io/96k_10_1ch.opus create mode 100644 test/assets/io/96k_10_2ch.opus create mode 100644 test/assets/io/96k_5_1ch.opus create mode 100644 test/assets/io/96k_5_2ch.opus create mode 100644 test/assets/io/generate_opus.py delete mode 100644 test/common_utils.py create mode 100644 test/common_utils/__init__.py create mode 100644 test/common_utils/backend_utils.py create mode 100644 test/common_utils/case_utils.py create mode 100644 test/common_utils/data_utils.py create mode 100644 test/common_utils/parameterized_utils.py create mode 100644 test/common_utils/sox_utils.py create mode 100644 test/common_utils/wav_utils.py create mode 100644 test/sox_io_backend/__init__.py create mode 100644 test/sox_io_backend/common.py create mode 100644 test/sox_io_backend/test_info.py create mode 100644 test/sox_io_backend/test_load.py create mode 100644 test/sox_io_backend/test_roundtrip.py create mode 100644 test/sox_io_backend/test_save.py create mode 100644 test/sox_io_backend/test_torchscript.py create mode 100644 third_party/CMakeLists.txt create mode 100755 third_party/build_codec_helper.sh create mode 100644 third_party/patch/libmad.patch create mode 100644 torchaudio/backend/sox_io_backend.py create mode 100644 torchaudio/csrc/sox_effects.cpp create mode 100644 torchaudio/csrc/sox_effects.h create mode 100644 torchaudio/csrc/sox_io.cpp create mode 100644 torchaudio/csrc/sox_io.h create mode 100644 torchaudio/csrc/sox_utils.cpp create mode 100644 torchaudio/csrc/sox_utils.h delete mode 100644 torchaudio/csrc/typedefs.cpp delete mode 100644 torchaudio/csrc/typedefs.h diff --git a/README.md b/README.md index 94f993ddaa..290bb73543 100644 --- a/README.md +++ b/README.md @@ -113,8 +113,8 @@ python setup.py install MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py install ``` -Alternatively, the build process can build SoX (and codecs such as libmad, lame and flac) statically and torchaudio can link them, by setting environment variable `BUILD_SOX=1`. -The build process will fetch and build SoX, liblame, libmad, flac before building extension. +Alternatively, the build process can build libsox and some optional codecs statically and torchaudio can link them, by setting environment variable `BUILD_SOX=1`. +The build process will fetch and build libmad, lame, flac, vorbis, opus, and libsox before building extension. This process requires `cmake` and `pkg-config`. ```bash # Linux diff --git a/build_tools/setup_helpers/build_third_party.sh b/build_tools/setup_helpers/build_third_party.sh deleted file mode 100755 index 9577776cc9..0000000000 --- a/build_tools/setup_helpers/build_third_party.sh +++ /dev/null @@ -1,50 +0,0 @@ -#!/bin/bash -# Build third party libraries (SoX, lame, libmad, and flac) -# Usage: ./build_thid_parth.sh [prefix] [download_only?=false] - -set -e - -this_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" -root_dir="${this_dir}/../.." - -prefix="${1:-}" -if [ -z "${prefix}" ]; then - prefix="${root_dir}" -fi -download_only="${2:-false}" - -tp_dir="${prefix}/third_party" -tmp_dir="${tp_dir}/tmp" -build_dir="${tp_dir}/build" - -mkdir -p "${tmp_dir}" "${build_dir}" - -. "${this_dir}/build_third_party_helper.sh" - -if ! found_lame "${build_dir}" ; then - get_lame "${tmp_dir}" - if [ "${download_only}" = "false" ]; then - build_lame "${tmp_dir}" "${build_dir}" - fi -fi - -if ! found_flac "${build_dir}" ; then - get_flac "${tmp_dir}" - if [ "${download_only}" = "false" ]; then - build_flac "${tmp_dir}" "${build_dir}" - fi -fi - -if ! found_mad "${build_dir}" ; then - get_mad "${tmp_dir}" - if [ "${download_only}" = "false" ]; then - build_mad "${tmp_dir}" "${build_dir}" - fi -fi - -if ! found_sox "${build_dir}" ; then - get_sox "${tmp_dir}" - if [ "${download_only}" = "false" ]; then - build_sox "${tmp_dir}" "${build_dir}" - fi -fi diff --git a/build_tools/setup_helpers/build_third_party_helper.sh b/build_tools/setup_helpers/build_third_party_helper.sh deleted file mode 100644 index 7cca812409..0000000000 --- a/build_tools/setup_helpers/build_third_party_helper.sh +++ /dev/null @@ -1,217 +0,0 @@ -#!/usr/bin/env bash - -set -euo pipefail - -# Global options -CURL_OPTS="-L --retry 10 --connect-timeout 5 --max-time 180" -MAKE_OPTS="-j" -CONFIG_OPTS="" - -if [ -z ${DEBUG+x} ]; then - CURL_OPTS="${CURL_OPTS} --silent --show-error" - MAKE_OPTS="${MAKE_OPTS} --quiet" - CONFIG_OPTS="${CONFIG_OPTS} --quiet" -fi - -all_found() { - dir="$1" - shift - while [ "$#" -gt 0 ]; do - if [ ! -f "${dir}/$1" ]; then - return 1 - fi - shift - done -} - - -found_lame() { - all_found "$1" 'include/lame/lame.h' 'lib/libmp3lame.a' -} - -found_flac() { - all_found "$1" \ - 'include/FLAC/format.h' \ - 'include/FLAC/stream_decoder.h' \ - 'include/FLAC/export.h' \ - 'include/FLAC/ordinals.h' \ - 'include/FLAC/all.h' \ - 'include/FLAC/assert.h' \ - 'include/FLAC/callback.h' \ - 'include/FLAC/metadata.h' \ - 'include/FLAC/stream_encoder.h' \ - 'include/FLAC++/export.h' \ - 'include/FLAC++/decoder.h' \ - 'include/FLAC++/all.h' \ - 'include/FLAC++/metadata.h' \ - 'include/FLAC++/encoder.h' \ - 'lib/libFLAC++.a' \ - 'lib/libFLAC.a' -} - -found_mad() { - all_found "$1" 'include/mad.h' 'lib/libmad.a' -} - -found_sox() { - all_found "$1" 'include/sox.h' 'lib/libsox.a' -} - -LAME="lame-3.99.5" -LAME_ARCHIVE="${LAME}.tar.gz" - -get_lame() { - work_dir="$1" - url="https://downloads.sourceforge.net/project/lame/lame/3.99/${LAME_ARCHIVE}" - ( - cd "${work_dir}" - if [ ! -d "${LAME}" ]; then - if [ ! -f "${LAME_ARCHIVE}" ]; then - printf "Fetching liblame from %s\n" "${url}" - curl $CURL_OPTS -O "${url}" - fi - fi - ) -} - -build_lame() { - work_dir="$1" - install_dir="$2" - ( - cd "${work_dir}" - if [ ! -d "${LAME}" ]; then - tar xfp "${LAME_ARCHIVE}" - fi - cd "${LAME}" - # build statically - printf "Building liblame\n" - if [ ! -f Makefile ]; then - ./configure ${CONFIG_OPTS} \ - --disable-shared --enable-static --prefix="${install_dir}" CFLAGS=-fPIC CXXFLAGS=-fPIC \ - --with-pic --disable-debug --disable-dependency-tracking --enable-nasm - fi - make ${MAKE_OPTS} > make.log 2>&1 - make ${MAKE_OPTS} install - ) -} - -FLAC="flac-1.3.2" -FLAC_ARCHIVE="${FLAC}.tar.xz" - -get_flac() { - work_dir="$1" - url="https://downloads.sourceforge.net/project/flac/flac-src/${FLAC_ARCHIVE}" - ( - cd "${work_dir}" - if [ ! -d "${FLAC}" ]; then - if [ ! -f "${FLAC_ARCHIVE}" ]; then - printf "Fetching flac from %s\n" "${url}" - curl $CURL_OPTS -O "${url}" - fi - fi - ) -} - -build_flac() { - work_dir="$1" - install_dir="$2" - ( - cd "${work_dir}" - if [ ! -d "${FLAC}" ]; then - tar xfp "${FLAC_ARCHIVE}" - fi - cd "${FLAC}" - # build statically - printf "Building flac\n" - if [ ! -f Makefile ]; then - ./configure ${CONFIG_OPTS} \ - --disable-shared --enable-static --prefix="${install_dir}" CFLAGS=-fPIC CXXFLAGS=-fPIC \ - --with-pic --disable-debug --disable-dependency-tracking - fi - make ${MAKE_OPTS} > make.log 2>&1 - make ${MAKE_OPTS} install - ) -} - -LIBMAD="libmad-0.15.1b" -LIBMAD_ARCHIVE="${LIBMAD}.tar.gz" - -get_mad() { - work_dir="$1" - url="https://downloads.sourceforge.net/project/mad/libmad/0.15.1b/${LIBMAD_ARCHIVE}" - ( - cd "${work_dir}" - if [ ! -d "${LIBMAD}" ]; then - if [ ! -f "${LIBMAD_ARCHIVE}" ]; then - printf "Fetching mad from %s\n" "${url}" - curl $CURL_OPTS -O "${url}" - fi - fi - ) -} - -build_mad() { - work_dir="$1" - install_dir="$2" - ( - cd "${work_dir}" - if [ ! -d "${LIBMAD}" ]; then - tar xfp "${LIBMAD_ARCHIVE}" - fi - cd "${LIBMAD}" - # build statically - printf "Building mad\n" - if [ ! -f Makefile ]; then - # See https://stackoverflow.com/a/12864879/23845 - sed -i.bak 's/-march=i486//' configure - ./configure ${CONFIG_OPTS} \ - --disable-shared --enable-static --prefix="${install_dir}" CFLAGS=-fPIC CXXFLAGS=-fPIC \ - --with-pic --disable-debug --disable-dependency-tracking - fi - make ${MAKE_OPTS} > make.log 2>&1 - make ${MAKE_OPTS} install - ) -} - -SOX="sox-14.4.2" -SOX_ARCHIVE="${SOX}.tar.bz2" - -get_sox() { - work_dir="$1" - url="https://downloads.sourceforge.net/project/sox/sox/14.4.2/${SOX_ARCHIVE}" - ( - cd "${work_dir}" - if [ ! -d "${SOX}" ]; then - if [ ! -f "${SOX_ARCHIVE}" ]; then - printf "Fetching SoX from %s\n" "${url}" - curl $CURL_OPTS -O "${url}" - fi - fi - ) -} - -build_sox() { - work_dir="$1" - install_dir="$2" - ( - cd "${work_dir}" - if [ ! -d "${SOX}" ]; then - tar xfp "${SOX_ARCHIVE}" - fi - cd "${SOX}" - # build statically - printf "Building SoX\n" - if [ ! -f Makefile ]; then - # --without-png makes OS X build less hazardous; somehow the build - # finds png and enables it. We don't want it; we'd need to package - # it statically if we do. - ./configure ${CONFIG_OPTS} --disable-shared --enable-static --prefix="${install_dir}" \ - LDFLAGS="-L${install_dir}/lib" CPPFLAGS="-I${install_dir}/include" \ - --with-lame --with-flac --with-mad --without-alsa --without-coreaudio \ - --without-png --without-oggvorbis --without-oss --without-sndfile \ - CFLAGS=-fPIC CXXFLAGS=-fPIC --with-pic --disable-debug --disable-dependency-tracking - fi - make ${MAKE_OPTS} > make.log 2>&1 - make ${MAKE_OPTS} install - ) -} diff --git a/build_tools/setup_helpers/extension.py b/build_tools/setup_helpers/extension.py index dcf61ab259..b9fb30e114 100644 --- a/build_tools/setup_helpers/extension.py +++ b/build_tools/setup_helpers/extension.py @@ -17,7 +17,7 @@ _ROOT_DIR = _THIS_DIR.parent.parent.resolve() _CSRC_DIR = _ROOT_DIR / 'torchaudio' / 'csrc' _TP_BASE_DIR = _ROOT_DIR / 'third_party' -_TP_INSTALL_DIR = _TP_BASE_DIR / 'build' +_TP_INSTALL_DIR = _TP_BASE_DIR / 'install' def _get_build_sox(): @@ -76,8 +76,20 @@ def _get_extra_objects(): # NOTE: The order of the library listed bellow matters. # # (the most important thing is that dependencies come after a library - # e.g., sox comes first) - libs = ['libsox.a', 'libmad.a', 'libFLAC.a', 'libmp3lame.a'] + # e.g., sox comes first, flac/vorbis comes before ogg, and + # vorbisenc/vorbisfile comes before vorbis + libs = [ + 'libsox.a', + 'libmad.a', + 'libFLAC.a', + 'libmp3lame.a', + 'libopusfile.a', + 'libopus.a', + 'libvorbisenc.a', + 'libvorbisfile.a', + 'libvorbis.a', + 'libogg.a', + ] for lib in libs: objs.append(str(_TP_INSTALL_DIR / 'lib' / lib)) return objs @@ -87,15 +99,19 @@ def _get_libraries(): return [] if _BUILD_SOX else ['sox'] -def _build_codecs(): +def _build_third_party(): + build_dir = str(_TP_BASE_DIR / 'build') + os.makedirs(build_dir, exist_ok=True) subprocess.run( - args=[str(_THIS_DIR / 'build_third_party.sh')], + args=['cmake', '..'], + cwd=build_dir, + check=True, + ) + subprocess.run( + args=['cmake', '--build', '.'], + cwd=build_dir, check=True, ) - - -def _configure_third_party(): - _build_codecs() _EXT_NAME = 'torchaudio._torchaudio' @@ -120,5 +136,5 @@ def get_ext_modules(debug=False): class BuildExtension(TorchBuildExtension): def build_extension(self, ext): if ext.name == _EXT_NAME and _BUILD_SOX: - _configure_third_party() + _build_third_party() super().build_extension(ext) diff --git a/packaging/build_conda.sh b/packaging/build_conda.sh index 3e011d312f..628eaf0bf8 100755 --- a/packaging/build_conda.sh +++ b/packaging/build_conda.sh @@ -6,7 +6,7 @@ script_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" export BUILD_TYPE="conda" export NO_CUDA_PACKAGE=1 -setup_env 0.6.0 +setup_env 0.7.0 export SOURCE_ROOT_DIR="$PWD" setup_conda_pytorch_constraint conda build $CONDA_CHANNEL_FLAGS --no-anaconda-upload --python "$PYTHON_VERSION" packaging/torchaudio diff --git a/packaging/build_wheel.sh b/packaging/build_wheel.sh index d08196cb20..3b25c00cd2 100755 --- a/packaging/build_wheel.sh +++ b/packaging/build_wheel.sh @@ -6,7 +6,7 @@ script_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" export BUILD_TYPE="wheel" export NO_CUDA_PACKAGE=1 -setup_env 0.6.0 +setup_env 0.7.0 setup_wheel_python pip_install numpy future setup_pip_pytorch_version diff --git a/packaging/pkg_helpers.bash b/packaging/pkg_helpers.bash index 4030128a98..3ec49a0269 100644 --- a/packaging/pkg_helpers.bash +++ b/packaging/pkg_helpers.bash @@ -171,6 +171,7 @@ setup_pip_pytorch_version() { else pip_install "torch==$PYTORCH_VERSION$PYTORCH_VERSION_SUFFIX" \ -f https://download.pytorch.org/whl/torch_stable.html \ + -f https://download.pytorch.org/whl/test/torch_test.html \ -f https://download.pytorch.org/whl/nightly/torch_nightly.html fi } @@ -184,7 +185,7 @@ setup_conda_pytorch_constraint() { export CONDA_CHANNEL_FLAGS="-c pytorch-nightly" export PYTORCH_VERSION="$(conda search --json 'pytorch[channel=pytorch-nightly]' | python -c "import sys, json, re; print(re.sub(r'\\+.*$', '', json.load(sys.stdin)['pytorch'][-1]['version']))")" else - export CONDA_CHANNEL_FLAGS="-c pytorch -c pytorch-nightly" + export CONDA_CHANNEL_FLAGS="-c pytorch -c pytorch-test -c pytorch-nightly" fi if [[ "$CU_VERSION" == cpu ]]; then export CONDA_PYTORCH_BUILD_CONSTRAINT="- pytorch==$PYTORCH_VERSION${PYTORCH_VERSION_SUFFIX}" diff --git a/setup.py b/setup.py index 5cf1ad680a..aab59b4d8a 100644 --- a/setup.py +++ b/setup.py @@ -12,7 +12,7 @@ # Creating the version file -version = '0.6.0a0' +version = '0.7.0a0' sha = 'Unknown' try: diff --git a/test/README.md b/test/README.md index 35a926c120..304bfa9c8e 100644 --- a/test/README.md +++ b/test/README.md @@ -41,6 +41,58 @@ The following test modules are defined for corresponding `torchaudio` module/fun - [assets/kaldi](./assets/kaldi): Contains Kaldi format matrix files used in [./test_compliance_kaldi.py](./test_compliance_kaldi.py). - [compliance](./compliance): Scripts used to generate above Kaldi matrix files. +### Waveforms for Testing Purposes + +When testing transforms we often need waveforms of specific type (ex: pure tone, noise, or voice), with specific bitrate (ex. 8 or 16 kHz) and number of channels (ex. mono, stereo). Below are some tips on how to construct waveforms and guidance around existing audio files. + +#### Load a Waveform from a File + +```python +filepath = common_utils.get_asset_path('filename.wav') +waveform, sample_rate = common_utils.load_wav(filepath) +``` + +*Note: Should you choose to contribute an audio file, please leave a comment in the issue or pull request, mentioning content source and licensing information. WAV files are preferred. Other formats should be used only when there is no alternative. (i.e. dataset implementation comes with hardcoded non-wav extension).* + +#### Pure Tone + +Code: + +```python +waveform = common_utils.get_sinusoid( + frequency=300, + sample_rate=16000, + duration=1, # seconds + n_channels=1, + dtype="float32", + device="cpu", +) +``` + +#### Noise + +Code: + +```python +tensor = common_utils.get_whitenoise() +``` + +Files: + +* `steam-train-whistle-daniel_simon.wav` + +#### Voice + +Files: + +* `CommonVoice/cv-corpus-4-2019-12-10/tt/clips/common_voice_tt_00000000.wav` +* `LibriSpeech/dev-clean/1272/128104/1272-128104-0000.flac` +* `LJSpeech-1.1/wavs/LJ001-0001.wav` +* `SpeechCommands/speech_commands_v0.02/go/0a9f9af7_nohash_0.wav` +* `VCTK-Corpus/wav48/p224/p224_002.wav` +* `waves_yesno/0_1_0_1_0_1_1_0.wav` +* `vad-go-stereo-44100.wav` +* `vad-go-mono-32000.wav` ## Adding test diff --git a/test/assets/CommonVoice/cv-corpus-4-2019-12-10/tt/clips/common_voice_tt_00000000.mp3 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+1,50 @@ +"""Generate opus file for testing load functions""" + +import argparse +import subprocess + +import scipy.io.wavfile +import torch + + +def _parse_args(): + parser = argparse.ArgumentParser( + description='Generate opus files for test' + ) + parser.add_argument('--num-channels', required=True, type=int) + parser.add_argument('--compression-level', required=True, type=int, choices=list(range(11))) + parser.add_argument('--bitrate', default='96k') + return parser.parse_args() + + +def convert_to_opus( + src_path, dst_path, + *, bitrate, compression_level): + """Convert audio file with `ffmpeg` command.""" + command = ['ffmpeg', '-y', '-i', src_path, '-c:a', 'libopus', '-b:a', bitrate] + if compression_level is not None: + command += ['-compression_level', str(compression_level)] + command += [dst_path] + print(' '.join(command)) + subprocess.run(command, check=True) + + +def _generate(num_channels, compression_level, bitrate): + org_path = 'original.wav' + ops_path = f'{bitrate}_{compression_level}_{num_channels}ch.opus' + + # Note: ffmpeg forces sample rate 48k Hz for opus https://stackoverflow.com/a/39186779 + # 1. generate original wav + data = torch.linspace(-32768, 32767, 32768, dtype=torch.int16).repeat([num_channels, 1]).t() + scipy.io.wavfile.write(org_path, 48000, data.numpy()) + # 2. convert to opus + convert_to_opus(org_path, ops_path, bitrate=bitrate, compression_level=compression_level) + + +def _main(): + args = _parse_args() + _generate(args.num_channels, args.compression_level, args.bitrate) + + +if __name__ == '__main__': + _main() diff --git a/test/common_utils.py b/test/common_utils.py deleted file mode 100644 index fe045d9db8..0000000000 --- a/test/common_utils.py +++ /dev/null @@ -1,171 +0,0 @@ -import os -import tempfile -import unittest -from typing import Union -from shutil import copytree - -import torch -from torch.testing._internal.common_utils import TestCase as PytorchTestCase -import torchaudio - -_TEST_DIR_PATH = os.path.dirname(os.path.realpath(__file__)) -BACKENDS = torchaudio.list_audio_backends() - - -def get_asset_path(*paths): - """Return full path of a test asset""" - return os.path.join(_TEST_DIR_PATH, 'assets', *paths) - - -def create_temp_assets_dir(): - """ - Creates a temporary directory and moves all files from test/assets there. - Returns a Tuple[string, TemporaryDirectory] which is the folder path - and object. - """ - tmp_dir = tempfile.TemporaryDirectory() - copytree(os.path.join(_TEST_DIR_PATH, "assets"), - os.path.join(tmp_dir.name, "assets")) - return tmp_dir.name, tmp_dir - - -def random_float_tensor(seed, size, a=22695477, c=1, m=2 ** 32): - """ Generates random tensors given a seed and size - https://en.wikipedia.org/wiki/Linear_congruential_generator - X_{n + 1} = (a * X_n + c) % m - Using Borland C/C++ values - - The tensor will have values between [0,1) - Inputs: - seed (int): an int - size (Tuple[int]): the size of the output tensor - a (int): the multiplier constant to the generator - c (int): the additive constant to the generator - m (int): the modulus constant to the generator - """ - num_elements = 1 - for s in size: - num_elements *= s - - arr = [(a * seed + c) % m] - for i in range(num_elements - 1): - arr.append((a * arr[i] + c) % m) - - return torch.tensor(arr).float().view(size) / m - - -def filter_backends_with_mp3(backends): - # Filter out backends that do not support mp3 - test_filepath = get_asset_path('steam-train-whistle-daniel_simon.mp3') - - def supports_mp3(backend): - torchaudio.set_audio_backend(backend) - try: - torchaudio.load(test_filepath) - return True - except (RuntimeError, ImportError): - return False - - return [backend for backend in backends if supports_mp3(backend)] - - -BACKENDS_MP3 = filter_backends_with_mp3(BACKENDS) - - -def set_audio_backend(backend): - """Allow additional backend value, 'default'""" - if backend == 'default': - if 'sox' in BACKENDS: - be = 'sox' - elif 'soundfile' in BACKENDS: - be = 'soundfile' - else: - raise unittest.SkipTest('No default backend available') - else: - be = backend - - torchaudio.set_audio_backend(be) - - -class TestBaseMixin: - """Mixin to provide consistent way to define device/dtype/backend aware TestCase""" - dtype = None - device = None - backend = None - - def setUp(self): - super().setUp() - set_audio_backend(self.backend) - - -class TorchaudioTestCase(TestBaseMixin, PytorchTestCase): - pass - - -skipIfNoSoxBackend = unittest.skipIf('sox' not in BACKENDS, 'Sox backend not available') -skipIfNoCuda = unittest.skipIf(not torch.cuda.is_available(), reason='CUDA not available') - - -def get_whitenoise( - *, - sample_rate: int = 16000, - duration: float = 1, # seconds - n_channels: int = 1, - seed: int = 0, - dtype: Union[str, torch.dtype] = "float32", - device: Union[str, torch.device] = "cpu", -): - """Generate pseudo audio data with whitenoise - - Args: - sample_rate: Sampling rate - duration: Length of the resulting Tensor in seconds. - n_channels: Number of channels - seed: Seed value used for random number generation. - Note that this function does not modify global random generator state. - dtype: Torch dtype - device: device - Returns: - Tensor: shape of (n_channels, sample_rate * duration) - """ - if isinstance(dtype, str): - dtype = getattr(torch, dtype) - shape = [n_channels, sample_rate * duration] - # According to the doc, folking rng on all CUDA devices is slow when there are many CUDA devices, - # so we only folk on CPU, generate values and move the data to the given device - with torch.random.fork_rng([]): - torch.random.manual_seed(seed) - tensor = torch.randn(shape, dtype=dtype, device='cpu') - tensor /= 2.0 - tensor.clamp_(-1.0, 1.0) - return tensor.to(device=device) - - -def get_sinusoid( - *, - frequency: float = 300, - sample_rate: int = 16000, - duration: float = 1, # seconds - n_channels: int = 1, - dtype: Union[str, torch.dtype] = "float32", - device: Union[str, torch.device] = "cpu", -): - """Generate pseudo audio data with sine wave. - - Args: - frequency: Frequency of sine wave - sample_rate: Sampling rate - duration: Length of the resulting Tensor in seconds. - n_channels: Number of channels - dtype: Torch dtype - device: device - - Returns: - Tensor: shape of (n_channels, sample_rate * duration) - """ - if isinstance(dtype, str): - dtype = getattr(torch, dtype) - pie2 = 2 * 3.141592653589793 - end = pie2 * frequency * duration - theta = torch.linspace(0, end, sample_rate * duration, dtype=dtype, device=device) - return torch.sin(theta, out=None).repeat([n_channels, 1]) diff --git a/test/common_utils/__init__.py b/test/common_utils/__init__.py new file mode 100644 index 0000000000..841cd26713 --- /dev/null +++ b/test/common_utils/__init__.py @@ -0,0 +1,31 @@ +from .data_utils import ( + get_asset_path, + get_whitenoise, + get_sinusoid, +) +from .backend_utils import ( + set_audio_backend, + BACKENDS, + BACKENDS_MP3, +) +from .case_utils import ( + TempDirMixin, + TestBaseMixin, + PytorchTestCase, + TorchaudioTestCase, + skipIfNoCuda, + skipIfNoExec, + skipIfNoModule, + skipIfNoExtension, + skipIfNoSoxBackend, +) +from .wav_utils import ( + get_wav_data, + normalize_wav, + load_wav, + save_wav, +) +from .parameterized_utils import ( + load_params, +) +from . import sox_utils diff --git a/test/common_utils/backend_utils.py b/test/common_utils/backend_utils.py new file mode 100644 index 0000000000..158fde87ed --- /dev/null +++ b/test/common_utils/backend_utils.py @@ -0,0 +1,41 @@ +import unittest + +import torchaudio + +from .import data_utils + + +BACKENDS = torchaudio.list_audio_backends() + + +def _filter_backends_with_mp3(backends): + # Filter out backends that do not support mp3 + test_filepath = data_utils.get_asset_path('steam-train-whistle-daniel_simon.mp3') + + def supports_mp3(backend): + torchaudio.set_audio_backend(backend) + try: + torchaudio.load(test_filepath) + return True + except (RuntimeError, ImportError): + return False + + return [backend for backend in backends if supports_mp3(backend)] + + +BACKENDS_MP3 = _filter_backends_with_mp3(BACKENDS) + + +def set_audio_backend(backend): + """Allow additional backend value, 'default'""" + if backend == 'default': + if 'sox' in BACKENDS: + be = 'sox' + elif 'soundfile' in BACKENDS: + be = 'soundfile' + else: + raise unittest.SkipTest('No default backend available') + else: + be = backend + + torchaudio.set_audio_backend(be) diff --git a/test/common_utils/case_utils.py b/test/common_utils/case_utils.py new file mode 100644 index 0000000000..f3b0c343a6 --- /dev/null +++ b/test/common_utils/case_utils.py @@ -0,0 +1,75 @@ +import shutil +import os.path +import tempfile +import unittest + +import torch +from torch.testing._internal.common_utils import TestCase as PytorchTestCase +import torchaudio +from torchaudio._internal.module_utils import is_module_available + +from .backend_utils import set_audio_backend + + +class TempDirMixin: + """Mixin to provide easy access to temp dir""" + temp_dir_ = None + base_temp_dir = None + temp_dir = None + + @classmethod + def setUpClass(cls): + super().setUpClass() + # If TORCHAUDIO_TEST_TEMP_DIR is set, use it instead of temporary directory. + # this is handy for debugging. + key = 'TORCHAUDIO_TEST_TEMP_DIR' + if key in os.environ: + cls.base_temp_dir = os.environ[key] + else: + cls.temp_dir_ = tempfile.TemporaryDirectory() + cls.base_temp_dir = cls.temp_dir_.name + + @classmethod + def tearDownClass(cls): + super().tearDownClass() + if isinstance(cls.temp_dir_, tempfile.TemporaryDirectory): + cls.temp_dir_.cleanup() + + def setUp(self): + super().setUp() + self.temp_dir = os.path.join(self.base_temp_dir, self.id()) + + def get_temp_path(self, *paths): + path = os.path.join(self.temp_dir, *paths) + os.makedirs(os.path.dirname(path), exist_ok=True) + return path + + +class TestBaseMixin: + """Mixin to provide consistent way to define device/dtype/backend aware TestCase""" + dtype = None + device = None + backend = None + + def setUp(self): + super().setUp() + set_audio_backend(self.backend) + + +class TorchaudioTestCase(TestBaseMixin, PytorchTestCase): + pass + + +def skipIfNoExec(cmd): + return unittest.skipIf(shutil.which(cmd) is None, f'`{cmd}` is not available') + + +def skipIfNoModule(module, display_name=None): + display_name = display_name or module + return unittest.skipIf(not is_module_available(module), f'"{display_name}" is not available') + + +skipIfNoSoxBackend = unittest.skipIf( + 'sox' not in torchaudio.list_audio_backends(), 'Sox backend not available') +skipIfNoCuda = unittest.skipIf(not torch.cuda.is_available(), reason='CUDA not available') +skipIfNoExtension = skipIfNoModule('torchaudio._torchaudio', 'torchaudio C++ extension') diff --git a/test/common_utils/data_utils.py b/test/common_utils/data_utils.py new file mode 100644 index 0000000000..b948ce334a --- /dev/null +++ b/test/common_utils/data_utils.py @@ -0,0 +1,94 @@ +import os.path +from typing import Union + +import torch + + +_TEST_DIR_PATH = os.path.realpath( + os.path.join(os.path.dirname(__file__), '..')) + + +def get_asset_path(*paths): + """Return full path of a test asset""" + return os.path.join(_TEST_DIR_PATH, 'assets', *paths) + + +def get_whitenoise( + *, + sample_rate: int = 16000, + duration: float = 1, # seconds + n_channels: int = 1, + seed: int = 0, + dtype: Union[str, torch.dtype] = "float32", + device: Union[str, torch.device] = "cpu", + channels_first=True, + scale_factor: float = 1, +): + """Generate pseudo audio data with whitenoise + Args: + sample_rate: Sampling rate + duration: Length of the resulting Tensor in seconds. + n_channels: Number of channels + seed: Seed value used for random number generation. + Note that this function does not modify global random generator state. + dtype: Torch dtype + device: device + channels_first: whether first dimension is n_channels + scale_factor: scale the Tensor before clamping and quantization + Returns: + Tensor: shape of (n_channels, sample_rate * duration) + """ + if isinstance(dtype, str): + dtype = getattr(torch, dtype) + if dtype not in [torch.float32, torch.int32, torch.int16, torch.uint8]: + raise NotImplementedError(f'dtype {dtype} is not supported.') + # According to the doc, folking rng on all CUDA devices is slow when there are many CUDA devices, + # so we only folk on CPU, generate values and move the data to the given device + with torch.random.fork_rng([]): + torch.random.manual_seed(seed) + tensor = torch.randn([sample_rate * duration], dtype=torch.float32, device='cpu') + tensor /= 2.0 + tensor *= scale_factor + tensor.clamp_(-1.0, 1.0) + if dtype == torch.int32: + tensor *= (tensor > 0) * 2147483647 + (tensor < 0) * 2147483648 + if dtype == torch.int16: + tensor *= (tensor > 0) * 32767 + (tensor < 0) * 32768 + if dtype == torch.uint8: + tensor *= (tensor > 0) * 127 + (tensor < 0) * 128 + tensor += 128 + tensor = tensor.to(dtype) + tensor = tensor.repeat([n_channels, 1]) + if not channels_first: + tensor = tensor.t() + return tensor.to(device=device) + + +def get_sinusoid( + *, + frequency: float = 300, + sample_rate: int = 16000, + duration: float = 1, # seconds + n_channels: int = 1, + dtype: Union[str, torch.dtype] = "float32", + device: Union[str, torch.device] = "cpu", +): + """Generate pseudo audio data with sine wave. + + Args: + frequency: Frequency of sine wave + sample_rate: Sampling rate + duration: Length of the resulting Tensor in seconds. + n_channels: Number of channels + dtype: Torch dtype + device: device + + Returns: + Tensor: shape of (n_channels, sample_rate * duration) + """ + if isinstance(dtype, str): + dtype = getattr(torch, dtype) + pie2 = 2 * 3.141592653589793 + end = pie2 * frequency * duration + theta = torch.linspace(0, end, sample_rate * duration, dtype=dtype, device=device) + return torch.sin(theta, out=None).repeat([n_channels, 1]) diff --git a/test/common_utils/parameterized_utils.py b/test/common_utils/parameterized_utils.py new file mode 100644 index 0000000000..24404a6edd --- /dev/null +++ b/test/common_utils/parameterized_utils.py @@ -0,0 +1,10 @@ +import json + +from parameterized import param + +from .data_utils import get_asset_path + + +def load_params(*paths): + with open(get_asset_path(*paths), 'r') as file: + return [param(json.loads(line)) for line in file] diff --git a/test/common_utils/sox_utils.py b/test/common_utils/sox_utils.py new file mode 100644 index 0000000000..cd1c247b72 --- /dev/null +++ b/test/common_utils/sox_utils.py @@ -0,0 +1,79 @@ +import subprocess + + +def get_encoding(dtype): + encodings = { + 'float32': 'floating-point', + 'int32': 'signed-integer', + 'int16': 'signed-integer', + 'uint8': 'unsigned-integer', + } + return encodings[dtype] + + +def get_bit_depth(dtype): + bit_depths = { + 'float32': 32, + 'int32': 32, + 'int16': 16, + 'uint8': 8, + } + return bit_depths[dtype] + + +def gen_audio_file( + path, sample_rate, num_channels, + *, encoding=None, bit_depth=None, compression=None, attenuation=None, duration=1, +): + """Generate synthetic audio file with `sox` command.""" + if path.endswith('.wav'): + raise RuntimeError( + 'Use get_wav_data and save_wav to generate wav file for accurate result.') + command = [ + 'sox', + '-V3', # verbose + '-R', + # -R is supposed to be repeatable, though the implementation looks suspicious + # and not setting the seed to a fixed value. + # https://fossies.org/dox/sox-14.4.2/sox_8c_source.html + # search "sox_globals.repeatable" + ] + if bit_depth is not None: + command += ['--bits', str(bit_depth)] + command += [ + '--rate', str(sample_rate), + '--null', # no input + '--channels', str(num_channels), + ] + if compression is not None: + command += ['--compression', str(compression)] + if bit_depth is not None: + command += ['--bits', str(bit_depth)] + if encoding is not None: + command += ['--encoding', str(encoding)] + command += [ + str(path), + 'synth', str(duration), # synthesizes for the given duration [sec] + 'sawtooth', '1', + # saw tooth covers the both ends of value range, which is a good property for test. + # similar to linspace(-1., 1.) + # this introduces bigger boundary effect than sine when converted to mp3 + ] + if attenuation is not None: + command += ['vol', f'-{attenuation}dB'] + print(' '.join(command)) + subprocess.run(command, check=True) + + +def convert_audio_file( + src_path, dst_path, + *, bit_depth=None, compression=None): + """Convert audio file with `sox` command.""" + command = ['sox', '-V3', '-R', str(src_path)] + if bit_depth is not None: + command += ['--bits', str(bit_depth)] + if compression is not None: + command += ['--compression', str(compression)] + command += [dst_path] + print(' '.join(command)) + subprocess.run(command, check=True) diff --git a/test/common_utils/wav_utils.py b/test/common_utils/wav_utils.py new file mode 100644 index 0000000000..bc122ec6cb --- /dev/null +++ b/test/common_utils/wav_utils.py @@ -0,0 +1,86 @@ +from typing import Optional + +import torch +import scipy.io.wavfile + + +def normalize_wav(tensor: torch.Tensor) -> torch.Tensor: + if tensor.dtype == torch.float32: + pass + elif tensor.dtype == torch.int32: + tensor = tensor.to(torch.float32) + tensor[tensor > 0] /= 2147483647. + tensor[tensor < 0] /= 2147483648. + elif tensor.dtype == torch.int16: + tensor = tensor.to(torch.float32) + tensor[tensor > 0] /= 32767. + tensor[tensor < 0] /= 32768. + elif tensor.dtype == torch.uint8: + tensor = tensor.to(torch.float32) - 128 + tensor[tensor > 0] /= 127. + tensor[tensor < 0] /= 128. + return tensor + + +def get_wav_data( + dtype: str, + num_channels: int, + *, + num_frames: Optional[int] = None, + normalize: bool = True, + channels_first: bool = True, +): + """Generate linear signal of the given dtype and num_channels + + Data range is + [-1.0, 1.0] for float32, + [-2147483648, 2147483647] for int32 + [-32768, 32767] for int16 + [0, 255] for uint8 + + num_frames allow to change the linear interpolation parameter. + Default values are 256 for uint8, else 1 << 16. + 1 << 16 as default is so that int16 value range is completely covered. + """ + dtype_ = getattr(torch, dtype) + + if num_frames is None: + if dtype == 'uint8': + num_frames = 256 + else: + num_frames = 1 << 16 + + if dtype == 'uint8': + base = torch.linspace(0, 255, num_frames, dtype=dtype_) + if dtype == 'float32': + base = torch.linspace(-1., 1., num_frames, dtype=dtype_) + if dtype == 'int32': + base = torch.linspace(-2147483648, 2147483647, num_frames, dtype=dtype_) + if dtype == 'int16': + base = torch.linspace(-32768, 32767, num_frames, dtype=dtype_) + data = base.repeat([num_channels, 1]) + if not channels_first: + data = data.transpose(1, 0) + if normalize: + data = normalize_wav(data) + return data + + +def load_wav(path: str, normalize=True, channels_first=True) -> torch.Tensor: + """Load wav file without torchaudio""" + sample_rate, data = scipy.io.wavfile.read(path) + data = torch.from_numpy(data.copy()) + if data.ndim == 1: + data = data.unsqueeze(1) + if normalize: + data = normalize_wav(data) + if channels_first: + data = data.transpose(1, 0) + return data, sample_rate + + +def save_wav(path, data, sample_rate, channels_first=True): + """Save wav file without torchaudio""" + if channels_first: + data = data.transpose(1, 0) + scipy.io.wavfile.write(path, sample_rate, data.numpy()) diff --git a/test/functional_cpu_test.py b/test/functional_cpu_test.py index 470d6ab770..ab5fdaed95 100644 --- a/test/functional_cpu_test.py +++ b/test/functional_cpu_test.py @@ -10,6 +10,31 @@ from .functional_impl import Lfilter +def random_float_tensor(seed, size, a=22695477, c=1, m=2 ** 32): + """ Generates random tensors given a seed and size + https://en.wikipedia.org/wiki/Linear_congruential_generator + X_{n + 1} = (a * X_n + c) % m + Using Borland C/C++ values + + The tensor will have values between [0,1) + Inputs: + seed (int): an int + size (Tuple[int]): the size of the output tensor + a (int): the multiplier constant to the generator + c (int): the additive constant to the generator + m (int): the modulus constant to the generator + """ + num_elements = 1 + for s in size: + num_elements *= s + + arr = [(a * seed + c) % m] + for i in range(num_elements - 1): + arr.append((a * arr[i] + c) % m) + + return torch.tensor(arr).float().view(size) / m + + class TestLFilterFloat32(Lfilter, common_utils.PytorchTestCase): dtype = torch.float32 device = torch.device('cpu') @@ -49,7 +74,7 @@ def _test_istft_is_inverse_of_stft(kwargs): for data_size in [(2, 20), (3, 15), (4, 10)]: for i in range(100): - sound = common_utils.random_float_tensor(i, data_size) + sound = random_float_tensor(i, data_size) stft = torch.stft(sound, **kwargs) estimate = torchaudio.functional.istft(stft, length=sound.size(1), **kwargs) @@ -211,8 +236,8 @@ def test_istft_of_sine(self): def _test_linearity_of_istft(self, data_size, kwargs, atol=1e-6, rtol=1e-8): for i in range(self.number_of_trials): - tensor1 = common_utils.random_float_tensor(i, data_size) - tensor2 = common_utils.random_float_tensor(i * 2, data_size) + tensor1 = random_float_tensor(i, data_size) + tensor2 = random_float_tensor(i * 2, data_size) a, b = torch.rand(2) istft1 = torchaudio.functional.istft(tensor1, **kwargs) istft2 = torchaudio.functional.istft(tensor2, **kwargs) @@ -274,8 +299,6 @@ def test_linearity_of_istft4(self): class TestDetectPitchFrequency(common_utils.TorchaudioTestCase): - backend = 'default' - def test_pitch(self): test_filepath_100 = common_utils.get_asset_path("100Hz_44100Hz_16bit_05sec.wav") test_filepath_440 = common_utils.get_asset_path("440Hz_44100Hz_16bit_05sec.wav") @@ -287,7 +310,7 @@ def test_pitch(self): ] for filename, freq_ref in tests: - waveform, sample_rate = torchaudio.load(filename) + waveform, sample_rate = common_utils.load_wav(filename) freq = torchaudio.functional.detect_pitch_frequency(waveform, sample_rate) diff --git a/test/kaldi_compatibility_impl.py b/test/kaldi_compatibility_impl.py index ec58a3fa44..7e51620f63 100644 --- a/test/kaldi_compatibility_impl.py +++ b/test/kaldi_compatibility_impl.py @@ -1,20 +1,19 @@ """Test suites for checking numerical compatibility against Kaldi""" -import json -import shutil -import unittest import subprocess import kaldi_io import torch import torchaudio.functional as F import torchaudio.compliance.kaldi +from parameterized import parameterized -from . import common_utils -from parameterized import parameterized, param - - -def _not_available(cmd): - return shutil.which(cmd) is None +from .common_utils import ( + TestBaseMixin, + load_params, + skipIfNoExec, + get_asset_path, + load_wav +) def _convert_args(**kwargs): @@ -49,19 +48,12 @@ def _run_kaldi(command, input_type, input_value): return torch.from_numpy(result.copy()) # copy supresses some torch warning -def _load_params(path): - with open(path, 'r') as file: - return [param(json.loads(line)) for line in file] - - -class Kaldi(common_utils.TestBaseMixin): - backend = 'sox' - +class Kaldi(TestBaseMixin): def assert_equal(self, output, *, expected, rtol=None, atol=None): expected = expected.to(dtype=self.dtype, device=self.device) self.assertEqual(output, expected, rtol=rtol, atol=atol) - @unittest.skipIf(_not_available('apply-cmvn-sliding'), '`apply-cmvn-sliding` not available') + @skipIfNoExec('apply-cmvn-sliding') def test_sliding_window_cmn(self): """sliding_window_cmn should be numerically compatible with apply-cmvn-sliding""" kwargs = { @@ -77,34 +69,34 @@ def test_sliding_window_cmn(self): kaldi_result = _run_kaldi(command, 'ark', tensor) self.assert_equal(result, expected=kaldi_result) - @parameterized.expand(_load_params(common_utils.get_asset_path('kaldi_test_fbank_args.json'))) - @unittest.skipIf(_not_available('compute-fbank-feats'), '`compute-fbank-feats` not available') + @parameterized.expand(load_params('kaldi_test_fbank_args.json')) + @skipIfNoExec('compute-fbank-feats') def test_fbank(self, kwargs): """fbank should be numerically compatible with compute-fbank-feats""" - wave_file = common_utils.get_asset_path('kaldi_file.wav') - waveform = torchaudio.load_wav(wave_file)[0].to(dtype=self.dtype, device=self.device) + wave_file = get_asset_path('kaldi_file.wav') + waveform = load_wav(wave_file, normalize=False)[0].to(dtype=self.dtype, device=self.device) result = torchaudio.compliance.kaldi.fbank(waveform, **kwargs) command = ['compute-fbank-feats'] + _convert_args(**kwargs) + ['scp:-', 'ark:-'] kaldi_result = _run_kaldi(command, 'scp', wave_file) self.assert_equal(result, expected=kaldi_result, rtol=1e-4, atol=1e-8) - @parameterized.expand(_load_params(common_utils.get_asset_path('kaldi_test_spectrogram_args.json'))) - @unittest.skipIf(_not_available('compute-spectrogram-feats'), '`compute-spectrogram-feats` not available') + @parameterized.expand(load_params('kaldi_test_spectrogram_args.json')) + @skipIfNoExec('compute-spectrogram-feats') def test_spectrogram(self, kwargs): """spectrogram should be numerically compatible with compute-spectrogram-feats""" - wave_file = common_utils.get_asset_path('kaldi_file.wav') - waveform = torchaudio.load_wav(wave_file)[0].to(dtype=self.dtype, device=self.device) + wave_file = get_asset_path('kaldi_file.wav') + waveform = load_wav(wave_file, normalize=False)[0].to(dtype=self.dtype, device=self.device) result = torchaudio.compliance.kaldi.spectrogram(waveform, **kwargs) command = ['compute-spectrogram-feats'] + _convert_args(**kwargs) + ['scp:-', 'ark:-'] kaldi_result = _run_kaldi(command, 'scp', wave_file) self.assert_equal(result, expected=kaldi_result, rtol=1e-4, atol=1e-8) - @parameterized.expand(_load_params(common_utils.get_asset_path('kaldi_test_mfcc_args.json'))) - @unittest.skipIf(_not_available('compute-mfcc-feats'), '`compute-mfcc-feats` not available') + @parameterized.expand(load_params('kaldi_test_mfcc_args.json')) + @skipIfNoExec('compute-mfcc-feats') def test_mfcc(self, kwargs): """mfcc should be numerically compatible with compute-mfcc-feats""" - wave_file = common_utils.get_asset_path('kaldi_file.wav') - waveform = torchaudio.load_wav(wave_file)[0].to(dtype=self.dtype, device=self.device) + wave_file = get_asset_path('kaldi_file.wav') + waveform = load_wav(wave_file, normalize=False)[0].to(dtype=self.dtype, device=self.device) result = torchaudio.compliance.kaldi.mfcc(waveform, **kwargs) command = ['compute-mfcc-feats'] + _convert_args(**kwargs) + ['scp:-', 'ark:-'] kaldi_result = _run_kaldi(command, 'scp', wave_file) diff --git a/test/sox_io_backend/__init__.py b/test/sox_io_backend/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/test/sox_io_backend/common.py b/test/sox_io_backend/common.py new file mode 100644 index 0000000000..eb85937236 --- /dev/null +++ b/test/sox_io_backend/common.py @@ -0,0 +1,2 @@ +def name_func(func, _, params): + return f'{func.__name__}_{"_".join(str(arg) for arg in params.args)}' diff --git a/test/sox_io_backend/test_info.py b/test/sox_io_backend/test_info.py new file mode 100644 index 0000000000..da5207a7e5 --- /dev/null +++ b/test/sox_io_backend/test_info.py @@ -0,0 +1,125 @@ +import itertools +from parameterized import parameterized + +from torchaudio.backend import sox_io_backend + +from ..common_utils import ( + TempDirMixin, + PytorchTestCase, + skipIfNoExec, + skipIfNoExtension, + get_asset_path, + get_wav_data, + save_wav, + sox_utils, +) +from .common import ( + name_func, +) + + +@skipIfNoExec('sox') +@skipIfNoExtension +class TestInfo(TempDirMixin, PytorchTestCase): + @parameterized.expand(list(itertools.product( + ['float32', 'int32', 'int16', 'uint8'], + [8000, 16000], + [1, 2], + )), name_func=name_func) + def test_wav(self, dtype, sample_rate, num_channels): + """`sox_io_backend.info` can check wav file correctly""" + duration = 1 + path = self.get_temp_path('data.wav') + data = get_wav_data(dtype, num_channels, normalize=False, num_frames=duration * sample_rate) + save_wav(path, data, sample_rate) + info = sox_io_backend.info(path) + assert info.sample_rate == sample_rate + assert info.num_frames == sample_rate * duration + assert info.num_channels == num_channels + + @parameterized.expand(list(itertools.product( + ['float32', 'int32', 'int16', 'uint8'], + [8000, 16000], + [4, 8, 16, 32], + )), name_func=name_func) + def test_wav_multiple_channels(self, dtype, sample_rate, num_channels): + """`sox_io_backend.info` can check wav file with channels more than 2 correctly""" + duration = 1 + path = self.get_temp_path('data.wav') + data = get_wav_data(dtype, num_channels, normalize=False, num_frames=duration * sample_rate) + save_wav(path, data, sample_rate) + info = sox_io_backend.info(path) + assert info.sample_rate == sample_rate + assert info.num_frames == sample_rate * duration + assert info.num_channels == num_channels + + @parameterized.expand(list(itertools.product( + [8000, 16000], + [1, 2], + [96, 128, 160, 192, 224, 256, 320], + )), name_func=name_func) + def test_mp3(self, sample_rate, num_channels, bit_rate): + """`sox_io_backend.info` can check mp3 file correctly""" + duration = 1 + path = self.get_temp_path('data.mp3') + sox_utils.gen_audio_file( + path, sample_rate, num_channels, + compression=bit_rate, duration=duration, + ) + info = sox_io_backend.info(path) + assert info.sample_rate == sample_rate + # mp3 does not preserve the number of samples + # assert info.num_frames == sample_rate * duration + assert info.num_channels == num_channels + + @parameterized.expand(list(itertools.product( + [8000, 16000], + [1, 2], + list(range(9)), + )), name_func=name_func) + def test_flac(self, sample_rate, num_channels, compression_level): + """`sox_io_backend.info` can check flac file correctly""" + duration = 1 + path = self.get_temp_path('data.flac') + sox_utils.gen_audio_file( + path, sample_rate, num_channels, + compression=compression_level, duration=duration, + ) + info = sox_io_backend.info(path) + assert info.sample_rate == sample_rate + assert info.num_frames == sample_rate * duration + assert info.num_channels == num_channels + + @parameterized.expand(list(itertools.product( + [8000, 16000], + [1, 2], + [-1, 0, 1, 2, 3, 3.6, 5, 10], + )), name_func=name_func) + def test_vorbis(self, sample_rate, num_channels, quality_level): + """`sox_io_backend.info` can check vorbis file correctly""" + duration = 1 + path = self.get_temp_path('data.vorbis') + sox_utils.gen_audio_file( + path, sample_rate, num_channels, + compression=quality_level, duration=duration, + ) + info = sox_io_backend.info(path) + assert info.sample_rate == sample_rate + assert info.num_frames == sample_rate * duration + assert info.num_channels == num_channels + + +@skipIfNoExtension +class TestInfoOpus(PytorchTestCase): + @parameterized.expand(list(itertools.product( + ['96k'], + [1, 2], + [0, 5, 10], + )), name_func=name_func) + def test_opus(self, bitrate, num_channels, compression_level): + """`sox_io_backend.info` can check opus file correcty""" + path = get_asset_path('io', f'{bitrate}_{compression_level}_{num_channels}ch.opus') + info = sox_io_backend.info(path) + assert info.sample_rate == 48000 + assert info.num_frames == 32768 + assert info.num_channels == num_channels diff --git a/test/sox_io_backend/test_load.py b/test/sox_io_backend/test_load.py new file mode 100644 index 0000000000..8366a01f83 --- /dev/null +++ b/test/sox_io_backend/test_load.py @@ -0,0 +1,263 @@ +import itertools + +from torchaudio.backend import sox_io_backend +from parameterized import parameterized + +from ..common_utils import ( + TempDirMixin, + PytorchTestCase, + skipIfNoExec, + skipIfNoExtension, + get_asset_path, + get_wav_data, + load_wav, + save_wav, + sox_utils, +) +from .common import ( + name_func, +) + + +class LoadTestBase(TempDirMixin, PytorchTestCase): + def assert_wav(self, dtype, sample_rate, num_channels, normalize, duration): + """`sox_io_backend.load` can load wav format correctly. + + Wav data loaded with sox_io backend should match those with scipy + """ + path = self.get_temp_path('reference.wav') + data = get_wav_data(dtype, num_channels, normalize=normalize, num_frames=duration * sample_rate) + save_wav(path, data, sample_rate) + expected = load_wav(path, normalize=normalize)[0] + data, sr = sox_io_backend.load(path, normalize=normalize) + assert sr == sample_rate + self.assertEqual(data, expected) + + def assert_mp3(self, sample_rate, num_channels, bit_rate, duration): + """`sox_io_backend.load` can load mp3 format. + + mp3 encoding introduces delay and boundary effects so + we create reference wav file from mp3 + + x + | + | 1. Generate mp3 with Sox + | + v 2. Convert to wav with Sox + mp3 ------------------------------> wav + | | + | 3. Load with torchaudio | 4. Load with scipy + | | + v v + tensor ----------> x <----------- tensor + 5. Compare + + Underlying assumptions are; + i. Conversion of mp3 to wav with Sox preserves data. + ii. Loading wav file with scipy is correct. + + By combining i & ii, step 2. and 4. allows to load reference mp3 data + without using torchaudio + """ + path = self.get_temp_path('1.original.mp3') + ref_path = self.get_temp_path('2.reference.wav') + + # 1. Generate mp3 with sox + sox_utils.gen_audio_file( + path, sample_rate, num_channels, + compression=bit_rate, duration=duration) + # 2. Convert to wav with sox + sox_utils.convert_audio_file(path, ref_path) + # 3. Load mp3 with torchaudio + data, sr = sox_io_backend.load(path) + # 4. Load wav with scipy + data_ref = load_wav(ref_path)[0] + # 5. Compare + assert sr == sample_rate + self.assertEqual(data, data_ref, atol=3e-03, rtol=1.3e-06) + + def assert_flac(self, sample_rate, num_channels, compression_level, duration): + """`sox_io_backend.load` can load flac format. + + This test takes the same strategy as mp3 to compare the result + """ + path = self.get_temp_path('1.original.flac') + ref_path = self.get_temp_path('2.reference.wav') + + # 1. Generate flac with sox + sox_utils.gen_audio_file( + path, sample_rate, num_channels, + compression=compression_level, bit_depth=16, duration=duration) + # 2. Convert to wav with sox + sox_utils.convert_audio_file(path, ref_path) + # 3. Load flac with torchaudio + data, sr = sox_io_backend.load(path) + # 4. Load wav with scipy + data_ref = load_wav(ref_path)[0] + # 5. Compare + assert sr == sample_rate + self.assertEqual(data, data_ref, atol=4e-05, rtol=1.3e-06) + + def assert_vorbis(self, sample_rate, num_channels, quality_level, duration): + """`sox_io_backend.load` can load vorbis format. + + This test takes the same strategy as mp3 to compare the result + """ + path = self.get_temp_path('1.original.vorbis') + ref_path = self.get_temp_path('2.reference.wav') + + # 1. Generate vorbis with sox + sox_utils.gen_audio_file( + path, sample_rate, num_channels, + compression=quality_level, bit_depth=16, duration=duration) + # 2. Convert to wav with sox + sox_utils.convert_audio_file(path, ref_path) + # 3. Load vorbis with torchaudio + data, sr = sox_io_backend.load(path) + # 4. Load wav with scipy + data_ref = load_wav(ref_path)[0] + # 5. Compare + assert sr == sample_rate + self.assertEqual(data, data_ref, atol=4e-05, rtol=1.3e-06) + + +@skipIfNoExec('sox') +@skipIfNoExtension +class TestLoad(LoadTestBase): + """Test the correctness of `sox_io_backend.load` for various formats""" + @parameterized.expand(list(itertools.product( + ['float32', 'int32', 'int16', 'uint8'], + [8000, 16000], + [1, 2], + [False, True], + )), name_func=name_func) + def test_wav(self, dtype, sample_rate, num_channels, normalize): + """`sox_io_backend.load` can load wav format correctly.""" + self.assert_wav(dtype, sample_rate, num_channels, normalize, duration=1) + + @parameterized.expand(list(itertools.product( + ['int16'], + [16000], + [2], + [False], + )), name_func=name_func) + def test_wav_large(self, dtype, sample_rate, num_channels, normalize): + """`sox_io_backend.load` can load large wav file correctly.""" + two_hours = 2 * 60 * 60 + self.assert_wav(dtype, sample_rate, num_channels, normalize, two_hours) + + @parameterized.expand(list(itertools.product( + ['float32', 'int32', 'int16', 'uint8'], + [4, 8, 16, 32], + )), name_func=name_func) + def test_multiple_channels(self, dtype, num_channels): + """`sox_io_backend.load` can load wav file with more than 2 channels.""" + sample_rate = 8000 + normalize = False + self.assert_wav(dtype, sample_rate, num_channels, normalize, duration=1) + + @parameterized.expand(list(itertools.product( + [8000, 16000, 44100], + [1, 2], + [96, 128, 160, 192, 224, 256, 320], + )), name_func=name_func) + def test_mp3(self, sample_rate, num_channels, bit_rate): + """`sox_io_backend.load` can load mp3 format correctly.""" + self.assert_mp3(sample_rate, num_channels, bit_rate, duration=1) + + @parameterized.expand(list(itertools.product( + [16000], + [2], + [128], + )), name_func=name_func) + def test_mp3_large(self, sample_rate, num_channels, bit_rate): + """`sox_io_backend.load` can load large mp3 file correctly.""" + two_hours = 2 * 60 * 60 + self.assert_mp3(sample_rate, num_channels, bit_rate, two_hours) + + @parameterized.expand(list(itertools.product( + [8000, 16000], + [1, 2], + list(range(9)), + )), name_func=name_func) + def test_flac(self, sample_rate, num_channels, compression_level): + """`sox_io_backend.load` can load flac format correctly.""" + self.assert_flac(sample_rate, num_channels, compression_level, duration=1) + + @parameterized.expand(list(itertools.product( + [16000], + [2], + [0], + )), name_func=name_func) + def test_flac_large(self, sample_rate, num_channels, compression_level): + """`sox_io_backend.load` can load large flac file correctly.""" + two_hours = 2 * 60 * 60 + self.assert_flac(sample_rate, num_channels, compression_level, two_hours) + + @parameterized.expand(list(itertools.product( + [8000, 16000], + [1, 2], + [-1, 0, 1, 2, 3, 3.6, 5, 10], + )), name_func=name_func) + def test_vorbis(self, sample_rate, num_channels, quality_level): + """`sox_io_backend.load` can load vorbis format correctly.""" + self.assert_vorbis(sample_rate, num_channels, quality_level, duration=1) + + @parameterized.expand(list(itertools.product( + [16000], + [2], + [10], + )), name_func=name_func) + def test_vorbis_large(self, sample_rate, num_channels, quality_level): + """`sox_io_backend.load` can load large vorbis file correctly.""" + two_hours = 2 * 60 * 60 + self.assert_vorbis(sample_rate, num_channels, quality_level, two_hours) + + @parameterized.expand(list(itertools.product( + ['96k'], + [1, 2], + [0, 5, 10], + )), name_func=name_func) + def test_opus(self, bitrate, num_channels, compression_level): + """`sox_io_backend.load` can load opus file correctly.""" + ops_path = get_asset_path('io', f'{bitrate}_{compression_level}_{num_channels}ch.opus') + wav_path = self.get_temp_path(f'{bitrate}_{compression_level}_{num_channels}ch.opus.wav') + sox_utils.convert_audio_file(ops_path, wav_path) + + expected, sample_rate = load_wav(wav_path) + found, sr = sox_io_backend.load(ops_path) + + assert sample_rate == sr + self.assertEqual(expected, found) + + +@skipIfNoExec('sox') +@skipIfNoExtension +class TestLoadParams(TempDirMixin, PytorchTestCase): + """Test the correctness of frame parameters of `sox_io_backend.load`""" + original = None + path = None + + def setUp(self): + super().setUp() + sample_rate = 8000 + self.original = get_wav_data('float32', num_channels=2) + self.path = self.get_temp_path('test.wave') + save_wav(self.path, self.original, sample_rate) + + @parameterized.expand(list(itertools.product( + [0, 1, 10, 100, 1000], + [-1, 1, 10, 100, 1000], + )), name_func=name_func) + def test_frame(self, frame_offset, num_frames): + """num_frames and frame_offset correctly specify the region of data""" + found, _ = sox_io_backend.load(self.path, frame_offset, num_frames) + frame_end = None if num_frames == -1 else frame_offset + num_frames + self.assertEqual(found, self.original[:, frame_offset:frame_end]) + + @parameterized.expand([(True, ), (False, )], name_func=name_func) + def test_channels_first(self, channels_first): + """channels_first swaps axes""" + found, _ = sox_io_backend.load(self.path, channels_first=channels_first) + expected = self.original if channels_first else self.original.transpose(1, 0) + self.assertEqual(found, expected) diff --git a/test/sox_io_backend/test_roundtrip.py b/test/sox_io_backend/test_roundtrip.py new file mode 100644 index 0000000000..2a051bebd5 --- /dev/null +++ b/test/sox_io_backend/test_roundtrip.py @@ -0,0 +1,52 @@ +import itertools + +from torchaudio.backend import sox_io_backend +from parameterized import parameterized + +from ..common_utils import ( + TempDirMixin, + PytorchTestCase, + skipIfNoExec, + skipIfNoExtension, + get_wav_data, +) +from .common import ( + name_func, +) + + +@skipIfNoExec('sox') +@skipIfNoExtension +class TestRoundTripIO(TempDirMixin, PytorchTestCase): + """save/load round trip should not degrade data for lossless formats""" + @parameterized.expand(list(itertools.product( + ['float32', 'int32', 'int16', 'uint8'], + [8000, 16000], + [1, 2], + )), name_func=name_func) + def test_wav(self, dtype, sample_rate, num_channels): + """save/load round trip should not degrade data for wav formats""" + original = get_wav_data(dtype, num_channels, normalize=False) + data = original + for i in range(10): + path = self.get_temp_path(f'{i}.wav') + sox_io_backend.save(path, data, sample_rate) + data, sr = sox_io_backend.load(path, normalize=False) + assert sr == sample_rate + self.assertEqual(original, data) + + @parameterized.expand(list(itertools.product( + [8000, 16000], + [1, 2], + list(range(9)), + )), name_func=name_func) + def test_flac(self, sample_rate, num_channels, compression_level): + """save/load round trip should not degrade data for flac formats""" + original = get_wav_data('float32', num_channels) + data = original + for i in range(10): + path = self.get_temp_path(f'{i}.flac') + sox_io_backend.save(path, data, sample_rate, compression=compression_level) + data, sr = sox_io_backend.load(path) + assert sr == sample_rate + self.assertEqual(original, data) diff --git a/test/sox_io_backend/test_save.py b/test/sox_io_backend/test_save.py new file mode 100644 index 0000000000..53588c456b --- /dev/null +++ b/test/sox_io_backend/test_save.py @@ -0,0 +1,304 @@ +import itertools + +from torchaudio.backend import sox_io_backend +from parameterized import parameterized + +from ..common_utils import ( + TempDirMixin, + PytorchTestCase, + skipIfNoExec, + skipIfNoExtension, + get_wav_data, + load_wav, + save_wav, + sox_utils, +) +from .common import ( + name_func, +) + + +class SaveTestBase(TempDirMixin, PytorchTestCase): + def assert_wav(self, dtype, sample_rate, num_channels, num_frames): + """`sox_io_backend.save` can save wav format.""" + path = self.get_temp_path('data.wav') + expected = get_wav_data(dtype, num_channels, num_frames=num_frames) + sox_io_backend.save(path, expected, sample_rate) + found, sr = load_wav(path) + assert sample_rate == sr + self.assertEqual(found, expected) + + def assert_mp3(self, sample_rate, num_channels, bit_rate, duration): + """`sox_io_backend.save` can save mp3 format. + + mp3 encoding introduces delay and boundary effects so + we convert the resulting mp3 to wav and compare the results there + + | + | 1. Generate original wav file with SciPy + | + v + -------------- wav ---------------- + | | + | 2.1. load with scipy | 3.1. Convert to mp3 with Sox + | then save with torchaudio | + v v + mp3 mp3 + | | + | 2.2. Convert to wav with Sox | 3.2. Convert to wav with Sox + | | + v v + wav wav + | | + | 2.3. load with scipy | 3.3. load with scipy + | | + v v + tensor -------> compare <--------- tensor + + """ + src_path = self.get_temp_path('1.reference.wav') + mp3_path = self.get_temp_path('2.1.torchaudio.mp3') + wav_path = self.get_temp_path('2.2.torchaudio.wav') + mp3_path_sox = self.get_temp_path('3.1.sox.mp3') + wav_path_sox = self.get_temp_path('3.2.sox.wav') + + # 1. Generate original wav + data = get_wav_data('float32', num_channels, normalize=True, num_frames=duration * sample_rate) + save_wav(src_path, data, sample_rate) + # 2.1. Convert the original wav to mp3 with torchaudio + sox_io_backend.save( + mp3_path, load_wav(src_path)[0], sample_rate, compression=bit_rate) + # 2.2. Convert the mp3 to wav with Sox + sox_utils.convert_audio_file(mp3_path, wav_path) + # 2.3. Load + found = load_wav(wav_path)[0] + + # 3.1. Convert the original wav to mp3 with SoX + sox_utils.convert_audio_file(src_path, mp3_path_sox, compression=bit_rate) + # 3.2. Convert the mp3 to wav with Sox + sox_utils.convert_audio_file(mp3_path_sox, wav_path_sox) + # 3.3. Load + expected = load_wav(wav_path_sox)[0] + + self.assertEqual(found, expected) + + def assert_flac(self, sample_rate, num_channels, compression_level, duration): + """`sox_io_backend.save` can save flac format. + + This test takes the same strategy as mp3 to compare the result + """ + src_path = self.get_temp_path('1.reference.wav') + flc_path = self.get_temp_path('2.1.torchaudio.flac') + wav_path = self.get_temp_path('2.2.torchaudio.wav') + flc_path_sox = self.get_temp_path('3.1.sox.flac') + wav_path_sox = self.get_temp_path('3.2.sox.wav') + + # 1. Generate original wav + data = get_wav_data('float32', num_channels, normalize=True, num_frames=duration * sample_rate) + save_wav(src_path, data, sample_rate) + # 2.1. Convert the original wav to flac with torchaudio + sox_io_backend.save( + flc_path, load_wav(src_path)[0], sample_rate, compression=compression_level) + # 2.2. Convert the flac to wav with Sox + # converting to 32 bit because flac file has 24 bit depth which scipy cannot handle. + sox_utils.convert_audio_file(flc_path, wav_path, bit_depth=32) + # 2.3. Load + found = load_wav(wav_path)[0] + + # 3.1. Convert the original wav to flac with SoX + sox_utils.convert_audio_file(src_path, flc_path_sox, compression=compression_level) + # 3.2. Convert the flac to wav with Sox + # converting to 32 bit because flac file has 24 bit depth which scipy cannot handle. + sox_utils.convert_audio_file(flc_path_sox, wav_path_sox, bit_depth=32) + # 3.3. Load + expected = load_wav(wav_path_sox)[0] + + self.assertEqual(found, expected) + + def _assert_vorbis(self, sample_rate, num_channels, quality_level, duration): + """`sox_io_backend.save` can save vorbis format. + + This test takes the same strategy as mp3 to compare the result + """ + src_path = self.get_temp_path('1.reference.wav') + vbs_path = self.get_temp_path('2.1.torchaudio.vorbis') + wav_path = self.get_temp_path('2.2.torchaudio.wav') + vbs_path_sox = self.get_temp_path('3.1.sox.vorbis') + wav_path_sox = self.get_temp_path('3.2.sox.wav') + + # 1. Generate original wav + data = get_wav_data('int16', num_channels, normalize=False, num_frames=duration * sample_rate) + save_wav(src_path, data, sample_rate) + # 2.1. Convert the original wav to vorbis with torchaudio + sox_io_backend.save( + vbs_path, load_wav(src_path)[0], sample_rate, compression=quality_level) + # 2.2. Convert the vorbis to wav with Sox + sox_utils.convert_audio_file(vbs_path, wav_path) + # 2.3. Load + found = load_wav(wav_path)[0] + + # 3.1. Convert the original wav to vorbis with SoX + sox_utils.convert_audio_file(src_path, vbs_path_sox, compression=quality_level) + # 3.2. Convert the vorbis to wav with Sox + sox_utils.convert_audio_file(vbs_path_sox, wav_path_sox) + # 3.3. Load + expected = load_wav(wav_path_sox)[0] + + # sox's vorbis encoding has some random boundary effect, which cause small number of + # samples yields higher descrepency than the others. + # so we allow small portions of data to be outside of absolute torelance. + # make sure to pass somewhat long duration + atol = 1.0e-4 + max_failure_allowed = 0.01 # this percent of samples are allowed to outside of atol. + failure_ratio = ((found - expected).abs() > atol).sum().item() / found.numel() + if failure_ratio > max_failure_allowed: + # it's failed and this will give a better error message. + self.assertEqual(found, expected, atol=atol, rtol=1.3e-6) + + def assert_vorbis(self, *args, **kwargs): + # sox's vorbis encoding has some randomness, so we run tests multiple time + max_retry = 5 + error = None + for _ in range(max_retry): + try: + self._assert_vorbis(*args, **kwargs) + break + except AssertionError as e: + error = e + else: + raise error + + +@skipIfNoExec('sox') +@skipIfNoExtension +class TestSave(SaveTestBase): + @parameterized.expand(list(itertools.product( + ['float32', 'int32', 'int16', 'uint8'], + [8000, 16000], + [1, 2], + )), name_func=name_func) + def test_wav(self, dtype, sample_rate, num_channels): + """`sox_io_backend.save` can save wav format.""" + self.assert_wav(dtype, sample_rate, num_channels, num_frames=None) + + @parameterized.expand(list(itertools.product( + ['float32'], + [16000], + [2], + )), name_func=name_func) + def test_wav_large(self, dtype, sample_rate, num_channels): + """`sox_io_backend.save` can save large wav file.""" + two_hours = 2 * 60 * 60 * sample_rate + self.assert_wav(dtype, sample_rate, num_channels, num_frames=two_hours) + + @parameterized.expand(list(itertools.product( + ['float32', 'int32', 'int16', 'uint8'], + [4, 8, 16, 32], + )), name_func=name_func) + def test_multiple_channels(self, dtype, num_channels): + """`sox_io_backend.save` can save wav with more than 2 channels.""" + sample_rate = 8000 + self.assert_wav(dtype, sample_rate, num_channels, num_frames=None) + + @parameterized.expand(list(itertools.product( + [8000, 16000], + [1, 2], + [-4.2, -0.2, 0, 0.2, 96, 128, 160, 192, 224, 256, 320], + )), name_func=name_func) + def test_mp3(self, sample_rate, num_channels, bit_rate): + """`sox_io_backend.save` can save mp3 format.""" + self.assert_mp3(sample_rate, num_channels, bit_rate, duration=1) + + @parameterized.expand(list(itertools.product( + [16000], + [2], + [128], + )), name_func=name_func) + def test_mp3_large(self, sample_rate, num_channels, bit_rate): + """`sox_io_backend.save` can save large mp3 file.""" + two_hours = 2 * 60 * 60 + self.assert_mp3(sample_rate, num_channels, bit_rate, duration=two_hours) + + @parameterized.expand(list(itertools.product( + [8000, 16000], + [1, 2], + list(range(9)), + )), name_func=name_func) + def test_flac(self, sample_rate, num_channels, compression_level): + """`sox_io_backend.save` can save flac format.""" + self.assert_flac(sample_rate, num_channels, compression_level, duration=1) + + @parameterized.expand(list(itertools.product( + [16000], + [2], + [0], + )), name_func=name_func) + def test_flac_large(self, sample_rate, num_channels, compression_level): + """`sox_io_backend.save` can save large flac file.""" + two_hours = 2 * 60 * 60 + self.assert_flac(sample_rate, num_channels, compression_level, duration=two_hours) + + @parameterized.expand(list(itertools.product( + [8000, 16000], + [1, 2], + [-1, 0, 1, 2, 3, 3.6, 5, 10], + )), name_func=name_func) + def test_vorbis(self, sample_rate, num_channels, quality_level): + """`sox_io_backend.save` can save vorbis format.""" + self.assert_vorbis(sample_rate, num_channels, quality_level, duration=20) + + # note: torchaudio can load large vorbis file, but cannot save large volbis file + # the following test causes Segmentation fault + # + ''' + @parameterized.expand(list(itertools.product( + [16000], + [2], + [10], + )), name_func=name_func) + def test_vorbis_large(self, sample_rate, num_channels, quality_level): + """`sox_io_backend.save` can save large vorbis file correctly.""" + two_hours = 2 * 60 * 60 + self.assert_vorbis(sample_rate, num_channels, quality_level, two_hours) + ''' + + +@skipIfNoExec('sox') +@skipIfNoExtension +class TestSaveParams(TempDirMixin, PytorchTestCase): + """Test the correctness of optional parameters of `sox_io_backend.save`""" + @parameterized.expand([(True, ), (False, )], name_func=name_func) + def test_channels_first(self, channels_first): + """channels_first swaps axes""" + path = self.get_temp_path('data.wav') + data = get_wav_data('int32', 2, channels_first=channels_first) + sox_io_backend.save( + path, data, 8000, channels_first=channels_first) + found = load_wav(path)[0] + expected = data if channels_first else data.transpose(1, 0) + self.assertEqual(found, expected) + + @parameterized.expand([ + 'float32', 'int32', 'int16', 'uint8' + ], name_func=name_func) + def test_noncontiguous(self, dtype): + """Noncontiguous tensors are saved correctly""" + path = self.get_temp_path('data.wav') + expected = get_wav_data(dtype, 4)[::2, ::2] + assert not expected.is_contiguous() + sox_io_backend.save(path, expected, 8000) + found = load_wav(path)[0] + self.assertEqual(found, expected) + + @parameterized.expand([ + 'float32', 'int32', 'int16', 'uint8', + ]) + def test_tensor_preserve(self, dtype): + """save function should not alter Tensor""" + path = self.get_temp_path('data.wav') + expected = get_wav_data(dtype, 4)[::2, ::2] + + data = expected.clone() + sox_io_backend.save(path, data, 8000) + + self.assertEqual(data, expected) diff --git a/test/sox_io_backend/test_torchscript.py b/test/sox_io_backend/test_torchscript.py new file mode 100644 index 0000000000..9a30aab0d2 --- /dev/null +++ b/test/sox_io_backend/test_torchscript.py @@ -0,0 +1,149 @@ +import itertools +from typing import Optional + +import torch +import torchaudio +from parameterized import parameterized + +from ..common_utils import ( + TempDirMixin, + TorchaudioTestCase, + skipIfNoExec, + skipIfNoExtension, + get_wav_data, + save_wav, + load_wav, + sox_utils, +) +from .common import ( + name_func, +) + + +def py_info_func(filepath: str) -> torchaudio.backend.sox_io_backend.AudioMetaData: + return torchaudio.info(filepath) + + +def py_load_func(filepath: str, normalize: bool, channels_first: bool): + return torchaudio.load( + filepath, normalize=normalize, channels_first=channels_first) + + +def py_save_func( + filepath: str, + tensor: torch.Tensor, + sample_rate: int, + channels_first: bool = True, + compression: Optional[float] = None, +): + torchaudio.save(filepath, tensor, sample_rate, channels_first, compression) + + +@skipIfNoExec('sox') +@skipIfNoExtension +class SoxIO(TempDirMixin, TorchaudioTestCase): + """TorchScript-ability Test suite for `sox_io_backend`""" + backend = 'sox_io' + + @parameterized.expand(list(itertools.product( + ['float32', 'int32', 'int16', 'uint8'], + [8000, 16000], + [1, 2], + )), name_func=name_func) + def test_info_wav(self, dtype, sample_rate, num_channels): + """`sox_io_backend.info` is torchscript-able and returns the same result""" + audio_path = self.get_temp_path(f'{dtype}_{sample_rate}_{num_channels}.wav') + data = get_wav_data(dtype, num_channels, normalize=False, num_frames=1 * sample_rate) + save_wav(audio_path, data, sample_rate) + + script_path = self.get_temp_path('info_func.zip') + torch.jit.script(py_info_func).save(script_path) + ts_info_func = torch.jit.load(script_path) + + py_info = py_info_func(audio_path) + ts_info = ts_info_func(audio_path) + + assert py_info.sample_rate == ts_info.sample_rate + assert py_info.num_frames == ts_info.num_frames + assert py_info.num_channels == ts_info.num_channels + + @parameterized.expand(list(itertools.product( + ['float32', 'int32', 'int16', 'uint8'], + [8000, 16000], + [1, 2], + [False, True], + [False, True], + )), name_func=name_func) + def test_load_wav(self, dtype, sample_rate, num_channels, normalize, channels_first): + """`sox_io_backend.load` is torchscript-able and returns the same result""" + audio_path = self.get_temp_path(f'test_load_{dtype}_{sample_rate}_{num_channels}_{normalize}.wav') + data = get_wav_data(dtype, num_channels, normalize=False, num_frames=1 * sample_rate) + save_wav(audio_path, data, sample_rate) + + script_path = self.get_temp_path('load_func.zip') + torch.jit.script(py_load_func).save(script_path) + ts_load_func = torch.jit.load(script_path) + + py_data, py_sr = py_load_func( + audio_path, normalize=normalize, channels_first=channels_first) + ts_data, ts_sr = ts_load_func( + audio_path, normalize=normalize, channels_first=channels_first) + + self.assertEqual(py_sr, ts_sr) + self.assertEqual(py_data, ts_data) + + @parameterized.expand(list(itertools.product( + ['float32', 'int32', 'int16', 'uint8'], + [8000, 16000], + [1, 2], + )), name_func=name_func) + def test_save_wav(self, dtype, sample_rate, num_channels): + script_path = self.get_temp_path('save_func.zip') + torch.jit.script(py_save_func).save(script_path) + ts_save_func = torch.jit.load(script_path) + + expected = get_wav_data(dtype, num_channels) + py_path = self.get_temp_path(f'test_save_py_{dtype}_{sample_rate}_{num_channels}.wav') + ts_path = self.get_temp_path(f'test_save_ts_{dtype}_{sample_rate}_{num_channels}.wav') + + py_save_func(py_path, expected, sample_rate, True, None) + ts_save_func(ts_path, expected, sample_rate, True, None) + + py_data, py_sr = load_wav(py_path) + ts_data, ts_sr = load_wav(ts_path) + + self.assertEqual(sample_rate, py_sr) + self.assertEqual(sample_rate, ts_sr) + self.assertEqual(expected, py_data) + self.assertEqual(expected, ts_data) + + @parameterized.expand(list(itertools.product( + [8000, 16000], + [1, 2], + list(range(9)), + )), name_func=name_func) + def test_save_flac(self, sample_rate, num_channels, compression_level): + script_path = self.get_temp_path('save_func.zip') + torch.jit.script(py_save_func).save(script_path) + ts_save_func = torch.jit.load(script_path) + + expected = get_wav_data('float32', num_channels) + py_path = self.get_temp_path(f'test_save_py_{sample_rate}_{num_channels}_{compression_level}.flac') + ts_path = self.get_temp_path(f'test_save_ts_{sample_rate}_{num_channels}_{compression_level}.flac') + + py_save_func(py_path, expected, sample_rate, True, compression_level) + ts_save_func(ts_path, expected, sample_rate, True, compression_level) + + # converting to 32 bit because flac file has 24 bit depth which scipy cannot handle. + py_path_wav = f'{py_path}.wav' + ts_path_wav = f'{ts_path}.wav' + sox_utils.convert_audio_file(py_path, py_path_wav, bit_depth=32) + sox_utils.convert_audio_file(ts_path, ts_path_wav, bit_depth=32) + + py_data, py_sr = load_wav(py_path_wav, normalize=True) + ts_data, ts_sr = load_wav(ts_path_wav, normalize=True) + + self.assertEqual(sample_rate, py_sr) + self.assertEqual(sample_rate, ts_sr) + self.assertEqual(expected, py_data) + self.assertEqual(expected, ts_data) diff --git a/test/test_backend.py b/test/test_backend.py index 1e8f9e4fd6..6b67cb2898 100644 --- a/test/test_backend.py +++ b/test/test_backend.py @@ -1,7 +1,4 @@ -import unittest - import torchaudio -from torchaudio._internal.module_utils import is_module_available from . import common_utils @@ -28,15 +25,19 @@ class TestBackendSwitch_NoBackend(BackendSwitchMixin, common_utils.TorchaudioTes backend_module = torchaudio.backend.no_backend -@unittest.skipIf( - not is_module_available('torchaudio._torchaudio'), - 'torchaudio C++ extension not available') +@common_utils.skipIfNoExtension class TestBackendSwitch_SoX(BackendSwitchMixin, common_utils.TorchaudioTestCase): backend = 'sox' backend_module = torchaudio.backend.sox_backend -@unittest.skipIf(not is_module_available('soundfile'), '"soundfile" not available') +@common_utils.skipIfNoExtension +class TestBackendSwitch_SoXIO(BackendSwitchMixin, common_utils.TorchaudioTestCase): + backend = 'sox_io' + backend_module = torchaudio.backend.sox_io_backend + + +@common_utils.skipIfNoModule('soundfile') class TestBackendSwitch_soundfile(BackendSwitchMixin, common_utils.TorchaudioTestCase): backend = 'soundfile' backend_module = torchaudio.backend.soundfile_backend diff --git a/test/test_datasets.py b/test/test_datasets.py index 3ac7fae8ac..c3b0c917da 100644 --- a/test/test_datasets.py +++ b/test/test_datasets.py @@ -46,9 +46,8 @@ def test_cmuarctic(self): data[0] -@common_utils.skipIfNoSoxBackend class TestCommonVoice(common_utils.TorchaudioTestCase): - backend = 'sox' + backend = 'default' path = common_utils.get_asset_path() def test_commonvoice(self): diff --git a/test/test_io.py b/test/test_io.py index f58f66ed11..2c8aece85e 100644 --- a/test/test_io.py +++ b/test/test_io.py @@ -1,11 +1,24 @@ import os import math +import shutil +import tempfile import unittest import torch import torchaudio -from .common_utils import BACKENDS, BACKENDS_MP3, create_temp_assets_dir +from .common_utils import BACKENDS, BACKENDS_MP3, get_asset_path + + +def create_temp_assets_dir(): + """ + Creates a temporary directory and moves all files from test/assets there. + Returns a Tuple[string, TemporaryDirectory] which is the folder path + and object. + """ + tmp_dir = tempfile.TemporaryDirectory() + shutil.copytree(get_asset_path(), os.path.join(tmp_dir.name, "assets")) + return tmp_dir.name, tmp_dir class Test_LoadSave(unittest.TestCase): @@ -17,11 +30,15 @@ class Test_LoadSave(unittest.TestCase): def test_1_save(self): for backend in BACKENDS_MP3: + if backend == 'sox_io': + continue with self.subTest(): torchaudio.set_audio_backend(backend) self._test_1_save(self.test_filepath, False) for backend in BACKENDS: + if backend == 'sox_io': + continue with self.subTest(): torchaudio.set_audio_backend(backend) self._test_1_save(self.test_filepath_wav, True) @@ -68,6 +85,8 @@ def _test_1_save(self, test_filepath, normalization): def test_1_save_sine(self): for backend in BACKENDS: + if backend == 'sox_io': + continue with self.subTest(): torchaudio.set_audio_backend(backend) self._test_1_save_sine() @@ -101,11 +120,15 @@ def _test_1_save_sine(self): def test_2_load(self): for backend in BACKENDS_MP3: + if backend == 'sox_io': + continue with self.subTest(): torchaudio.set_audio_backend(backend) self._test_2_load(self.test_filepath, 278756) for backend in BACKENDS: + if backend == 'sox_io': + continue with self.subTest(): torchaudio.set_audio_backend(backend) self._test_2_load(self.test_filepath_wav, 276858) @@ -142,6 +165,8 @@ def _test_2_load(self, test_filepath, length): def test_2_load_nonormalization(self): for backend in BACKENDS_MP3: + if backend == 'sox_io': + continue with self.subTest(): torchaudio.set_audio_backend(backend) self._test_2_load_nonormalization(self.test_filepath, 278756) @@ -159,6 +184,8 @@ def _test_2_load_nonormalization(self, test_filepath, length): def test_3_load_and_save_is_identity(self): for backend in BACKENDS: + if backend == 'sox_io': + continue with self.subTest(): torchaudio.set_audio_backend(backend) self._test_3_load_and_save_is_identity() @@ -197,6 +224,8 @@ def _test_3_load_and_save_is_identity_across_backend(self, backend1, backend2): def test_4_load_partial(self): for backend in BACKENDS_MP3: + if backend == 'sox_io': + continue with self.subTest(): torchaudio.set_audio_backend(backend) self._test_4_load_partial() @@ -239,6 +268,8 @@ def _test_4_load_partial(self): def test_5_get_info(self): for backend in BACKENDS: + if backend == 'sox_io': + continue with self.subTest(): torchaudio.set_audio_backend(backend) self._test_5_get_info() diff --git a/test/test_librosa_compatibility.py b/test/test_librosa_compatibility.py index 62e9d3ca88..aa933535e8 100644 --- a/test/test_librosa_compatibility.py +++ b/test/test_librosa_compatibility.py @@ -160,7 +160,8 @@ class TestTransforms(common_utils.TorchaudioTestCase): """Test suite for functions in `transforms` module.""" def assert_compatibilities(self, n_fft, hop_length, power, n_mels, n_mfcc, sample_rate): common_utils.set_audio_backend('default') - sound, sample_rate = _load_audio_asset('sinewave.wav') + path = common_utils.get_asset_path('sinewave.wav') + sound, sample_rate = common_utils.load_wav(path) sound_librosa = sound.cpu().numpy().squeeze() # (64000) # test core spectrogram @@ -300,9 +301,9 @@ def test_InverseMelScale(self): hop_length = n_fft // 4 # Prepare mel spectrogram input. We use torchaudio to compute one. - common_utils.set_audio_backend('default') - sound, sample_rate = _load_audio_asset( - 'steam-train-whistle-daniel_simon.wav', offset=2**10, num_frames=2**14) + path = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') + sound, sample_rate = common_utils.load_wav(path) + sound = sound[:, 2**10:2**10 + 2**14] sound = sound.mean(dim=0, keepdim=True) spec_orig = F.spectrogram( sound, pad=0, window=torch.hann_window(n_fft), n_fft=n_fft, diff --git a/test/test_models.py b/test/test_models.py index 7bd3f3819d..c54a57cebd 100644 --- a/test/test_models.py +++ b/test/test_models.py @@ -1,8 +1,10 @@ import torch -from torchaudio.models import Wav2Letter, _MelResNet +from torchaudio.models import Wav2Letter, _MelResNet, _UpsampleNetwork, _WaveRNN +from . import common_utils -class TestWav2Letter: + +class TestWav2Letter(common_utils.TorchaudioTestCase): def test_waveform(self): batch_size = 2 @@ -31,21 +33,110 @@ def test_mfcc(self): assert out.size() == (batch_size, num_classes, 2) -class TestMelResNet: +class TestMelResNet(common_utils.TorchaudioTestCase): def test_waveform(self): + """Validate the output dimensions of a _MelResNet block. + """ - batch_size = 2 - num_features = 200 - input_dims = 100 - output_dims = 128 - res_blocks = 10 - hidden_dims = 128 - pad = 2 + n_batch = 2 + n_time = 200 + n_freq = 100 + n_output = 128 + n_res_block = 10 + n_hidden = 128 + kernel_size = 5 - model = _MelResNet(res_blocks, input_dims, hidden_dims, output_dims, pad) + model = _MelResNet(n_res_block, n_freq, n_hidden, n_output, kernel_size) - x = torch.rand(batch_size, input_dims, num_features) + x = torch.rand(n_batch, n_freq, n_time) out = model(x) - assert out.size() == (batch_size, output_dims, num_features - pad * 2) + assert out.size() == (n_batch, n_output, n_time - kernel_size + 1) + + +class TestUpsampleNetwork(common_utils.TorchaudioTestCase): + + def test_waveform(self): + """Validate the output dimensions of a _UpsampleNetwork block. + """ + + upsample_scales = [5, 5, 8] + n_batch = 2 + n_time = 200 + n_freq = 100 + n_output = 256 + n_res_block = 10 + n_hidden = 128 + kernel_size = 5 + + total_scale = 1 + for upsample_scale in upsample_scales: + total_scale *= upsample_scale + + model = _UpsampleNetwork(upsample_scales, n_res_block, n_freq, n_hidden, n_output, kernel_size) + + x = torch.rand(n_batch, n_freq, n_time) + out1, out2 = model(x) + + assert out1.size() == (n_batch, n_freq, total_scale * (n_time - kernel_size + 1)) + assert out2.size() == (n_batch, n_output, total_scale * (n_time - kernel_size + 1)) + + +class TestWaveRNN(common_utils.TorchaudioTestCase): + + def test_waveform(self): + """Validate the output dimensions of a _WaveRNN model in waveform mode. + """ + + upsample_scales = [5, 5, 8] + n_rnn = 512 + n_fc = 512 + n_bits = 9 + sample_rate = 24000 + hop_length = 200 + n_batch = 2 + n_time = 200 + n_freq = 100 + n_output = 256 + n_res_block = 10 + n_hidden = 128 + kernel_size = 5 + mode = 'waveform' + + model = _WaveRNN(upsample_scales, n_bits, sample_rate, hop_length, n_res_block, + n_rnn, n_fc, kernel_size, n_freq, n_hidden, n_output, mode) + + x = torch.rand(n_batch, 1, hop_length * (n_time - kernel_size + 1)) + mels = torch.rand(n_batch, 1, n_freq, n_time) + out = model(x, mels) + + assert out.size() == (n_batch, 1, hop_length * (n_time - kernel_size + 1), 2 ** n_bits) + + def test_mol(self): + """Validate the output dimensions of a _WaveRNN model in mol mode. + """ + + upsample_scales = [5, 5, 8] + n_rnn = 512 + n_fc = 512 + n_bits = 9 + sample_rate = 24000 + hop_length = 200 + n_batch = 2 + n_time = 200 + n_freq = 100 + n_output = 256 + n_res_block = 10 + n_hidden = 128 + kernel_size = 5 + mode = 'mol' + + model = _WaveRNN(upsample_scales, n_bits, sample_rate, hop_length, n_res_block, + n_rnn, n_fc, kernel_size, n_freq, n_hidden, n_output, mode) + + x = torch.rand(n_batch, 1, hop_length * (n_time - kernel_size + 1)) + mels = torch.rand(n_batch, 1, n_freq, n_time) + out = model(x, mels) + + assert out.size() == (n_batch, 1, hop_length * (n_time - kernel_size + 1), 30) diff --git a/test/test_sox_compatibility.py b/test/test_sox_compatibility.py index 23d58a3fb1..a5d9f8633c 100644 --- a/test/test_sox_compatibility.py +++ b/test/test_sox_compatibility.py @@ -9,12 +9,30 @@ @common_utils.skipIfNoSoxBackend -class TestFunctionalFiltering(common_utils.TorchaudioTestCase): +class TestFunctionalFiltering(common_utils.TempDirMixin, common_utils.TorchaudioTestCase): backend = 'sox' + def setUp(self): + # 1. Create int16 signal to save as PCM wav + # 2. Write to temp file + # 3. Load temp file into tensor to reuse in downstream tests + # Prefer to use common_utils.load_wav() but this implementation does + # not match torchaudio.load and errors on downstream tests + super().setUp() + + self.NOISE_SAMPLE_RATE = 44100 # N.B. 44.1 kHz required by SoX deemph effect + noise_waveform_as_int = common_utils.get_whitenoise( + sample_rate=self.NOISE_SAMPLE_RATE, duration=5, dtype=torch.int16, scale_factor=0.9, + ) + self.noise_filepath = self.get_temp_path("whitenoise.wav") + common_utils.save_wav( + self.noise_filepath, noise_waveform_as_int, self.NOISE_SAMPLE_RATE + ) + self.noise_waveform, _ = torchaudio.load(self.noise_filepath, normalization=True) + def test_gain(self): test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') - waveform, _ = torchaudio.load(test_filepath) + waveform, _ = common_utils.load_wav(test_filepath) waveform_gain = F.gain(waveform, 3) self.assertTrue(waveform_gain.abs().max().item(), 1.) @@ -28,7 +46,7 @@ def test_gain(self): def test_dither(self): test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') - waveform, _ = torchaudio.load(test_filepath) + waveform, _ = common_utils.load_wav(test_filepath) waveform_dithered = F.dither(waveform) waveform_dithered_noiseshaped = F.dither(waveform, noise_shaping=True) @@ -48,7 +66,7 @@ def test_dither(self): def test_vctk_transform_pipeline(self): test_filepath_vctk = common_utils.get_asset_path('VCTK-Corpus', 'wav48', 'p224', 'p224_002.wav') - wf_vctk, sr_vctk = torchaudio.load(test_filepath_vctk) + wf_vctk, sr_vctk = common_utils.load_wav(test_filepath_vctk) # rate sample = T.Resample(sr_vctk, 16000, resampling_method='sinc_interpolation') @@ -71,17 +89,14 @@ def test_lowpass(self): """ Test biquad lowpass filter, compare to SoX implementation """ - cutoff_freq = 3000 - noise_filepath = common_utils.get_asset_path('whitenoise.wav') E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(noise_filepath) + E.set_input_file(self.noise_filepath) E.append_effect_to_chain("lowpass", [cutoff_freq]) sox_output_waveform, sr = E.sox_build_flow_effects() - waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.lowpass_biquad(waveform, sample_rate, cutoff_freq) + output_waveform = F.lowpass_biquad(self.noise_waveform, self.NOISE_SAMPLE_RATE, cutoff_freq) self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @@ -89,37 +104,30 @@ def test_highpass(self): """ Test biquad highpass filter, compare to SoX implementation """ - cutoff_freq = 2000 - noise_filepath = common_utils.get_asset_path('whitenoise.wav') E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(noise_filepath) + E.set_input_file(self.noise_filepath) E.append_effect_to_chain("highpass", [cutoff_freq]) sox_output_waveform, sr = E.sox_build_flow_effects() - waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.highpass_biquad(waveform, sample_rate, cutoff_freq) + output_waveform = F.highpass_biquad(self.noise_waveform, self.NOISE_SAMPLE_RATE, cutoff_freq) - # TBD - this fails at the 1e-4 level, debug why - self.assertEqual(output_waveform, sox_output_waveform, atol=1e-3, rtol=1e-5) + self.assertEqual(output_waveform, sox_output_waveform, atol=1.5e-3, rtol=1e-5) def test_allpass(self): """ Test biquad allpass filter, compare to SoX implementation """ - central_freq = 1000 q = 0.707 - noise_filepath = common_utils.get_asset_path('whitenoise.wav') E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(noise_filepath) + E.set_input_file(self.noise_filepath) E.append_effect_to_chain("allpass", [central_freq, str(q) + 'q']) sox_output_waveform, sr = E.sox_build_flow_effects() - waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.allpass_biquad(waveform, sample_rate, central_freq, q) + output_waveform = F.allpass_biquad(self.noise_waveform, self.NOISE_SAMPLE_RATE, central_freq, q) self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @@ -127,19 +135,17 @@ def test_bandpass_with_csg(self): """ Test biquad bandpass filter, compare to SoX implementation """ - central_freq = 1000 q = 0.707 const_skirt_gain = True - noise_filepath = common_utils.get_asset_path('whitenoise.wav') E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(noise_filepath) + E.set_input_file(self.noise_filepath) E.append_effect_to_chain("bandpass", ["-c", central_freq, str(q) + 'q']) sox_output_waveform, sr = E.sox_build_flow_effects() - waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.bandpass_biquad(waveform, sample_rate, central_freq, q, const_skirt_gain) + output_waveform = F.bandpass_biquad(self.noise_waveform, self.NOISE_SAMPLE_RATE, + central_freq, q, const_skirt_gain) self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @@ -147,19 +153,17 @@ def test_bandpass_without_csg(self): """ Test biquad bandpass filter, compare to SoX implementation """ - central_freq = 1000 q = 0.707 const_skirt_gain = False - noise_filepath = common_utils.get_asset_path('whitenoise.wav') E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(noise_filepath) + E.set_input_file(self.noise_filepath) E.append_effect_to_chain("bandpass", [central_freq, str(q) + 'q']) sox_output_waveform, sr = E.sox_build_flow_effects() - waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.bandpass_biquad(waveform, sample_rate, central_freq, q, const_skirt_gain) + output_waveform = F.bandpass_biquad(self.noise_waveform, self.NOISE_SAMPLE_RATE, + central_freq, q, const_skirt_gain) self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @@ -167,18 +171,16 @@ def test_bandreject(self): """ Test biquad bandreject filter, compare to SoX implementation """ - central_freq = 1000 q = 0.707 - noise_filepath = common_utils.get_asset_path('whitenoise.wav') E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(noise_filepath) + E.set_input_file(self.noise_filepath) E.append_effect_to_chain("bandreject", [central_freq, str(q) + 'q']) sox_output_waveform, sr = E.sox_build_flow_effects() - waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.bandreject_biquad(waveform, sample_rate, central_freq, q) + output_waveform = F.bandreject_biquad(self.noise_waveform, self.NOISE_SAMPLE_RATE, + central_freq, q) self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @@ -186,19 +188,16 @@ def test_band_with_noise(self): """ Test biquad band filter with noise mode, compare to SoX implementation """ - central_freq = 1000 q = 0.707 noise = True - noise_filepath = common_utils.get_asset_path('whitenoise.wav') E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(noise_filepath) + E.set_input_file(self.noise_filepath) E.append_effect_to_chain("band", ["-n", central_freq, str(q) + 'q']) sox_output_waveform, sr = E.sox_build_flow_effects() - waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.band_biquad(waveform, sample_rate, central_freq, q, noise) + output_waveform = F.band_biquad(self.noise_waveform, self.NOISE_SAMPLE_RATE, central_freq, q, noise) self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @@ -211,14 +210,12 @@ def test_band_without_noise(self): q = 0.707 noise = False - noise_filepath = common_utils.get_asset_path('whitenoise.wav') E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(noise_filepath) + E.set_input_file(self.noise_filepath) E.append_effect_to_chain("band", [central_freq, str(q) + 'q']) sox_output_waveform, sr = E.sox_build_flow_effects() - waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.band_biquad(waveform, sample_rate, central_freq, q, noise) + output_waveform = F.band_biquad(self.noise_waveform, self.NOISE_SAMPLE_RATE, central_freq, q, noise) self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @@ -231,14 +228,12 @@ def test_treble(self): q = 0.707 gain = 40 - noise_filepath = common_utils.get_asset_path('whitenoise.wav') E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(noise_filepath) + E.set_input_file(self.noise_filepath) E.append_effect_to_chain("treble", [gain, central_freq, str(q) + 'q']) sox_output_waveform, sr = E.sox_build_flow_effects() - waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.treble_biquad(waveform, sample_rate, gain, central_freq, q) + output_waveform = F.treble_biquad(self.noise_waveform, self.NOISE_SAMPLE_RATE, gain, central_freq, q) self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @@ -251,14 +246,12 @@ def test_bass(self): q = 0.707 gain = 40 - noise_filepath = common_utils.get_asset_path('whitenoise.wav') E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(noise_filepath) + E.set_input_file(self.noise_filepath) E.append_effect_to_chain("bass", [gain, central_freq, str(q) + 'q']) sox_output_waveform, sr = E.sox_build_flow_effects() - waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.bass_biquad(waveform, sample_rate, gain, central_freq, q) + output_waveform = F.bass_biquad(self.noise_waveform, self.NOISE_SAMPLE_RATE, gain, central_freq, q) self.assertEqual(output_waveform, sox_output_waveform, atol=1.5e-4, rtol=1e-5) @@ -266,15 +259,12 @@ def test_deemph(self): """ Test biquad deemph filter, compare to SoX implementation """ - - noise_filepath = common_utils.get_asset_path('whitenoise.wav') E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(noise_filepath) + E.set_input_file(self.noise_filepath) E.append_effect_to_chain("deemph") sox_output_waveform, sr = E.sox_build_flow_effects() - waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.deemph_biquad(waveform, sample_rate) + output_waveform = F.deemph_biquad(self.noise_waveform, self.NOISE_SAMPLE_RATE) self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @@ -282,15 +272,12 @@ def test_riaa(self): """ Test biquad riaa filter, compare to SoX implementation """ - - noise_filepath = common_utils.get_asset_path('whitenoise.wav') E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(noise_filepath) + E.set_input_file(self.noise_filepath) E.append_effect_to_chain("riaa") sox_output_waveform, sr = E.sox_build_flow_effects() - waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.riaa_biquad(waveform, sample_rate) + output_waveform = F.riaa_biquad(self.noise_waveform, self.NOISE_SAMPLE_RATE) self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @@ -299,14 +286,13 @@ def test_contrast(self): Test contrast effect, compare to SoX implementation """ enhancement_amount = 80. - noise_filepath = common_utils.get_asset_path('whitenoise.wav') + E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(noise_filepath) + E.set_input_file(self.noise_filepath) E.append_effect_to_chain("contrast", [enhancement_amount]) sox_output_waveform, sr = E.sox_build_flow_effects() - waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.contrast(waveform, enhancement_amount) + output_waveform = F.contrast(self.noise_waveform, enhancement_amount) self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @@ -316,14 +302,13 @@ def test_dcshift_with_limiter(self): """ shift = 0.5 limiter_gain = 0.05 - noise_filepath = common_utils.get_asset_path('whitenoise.wav') + E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(noise_filepath) + E.set_input_file(self.noise_filepath) E.append_effect_to_chain("dcshift", [shift, limiter_gain]) sox_output_waveform, sr = E.sox_build_flow_effects() - waveform, _ = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.dcshift(waveform, shift, limiter_gain) + output_waveform = F.dcshift(self.noise_waveform, shift, limiter_gain) self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @@ -332,14 +317,13 @@ def test_dcshift_without_limiter(self): Test dcshift effect, compare to SoX implementation """ shift = 0.6 - noise_filepath = common_utils.get_asset_path('whitenoise.wav') + E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(noise_filepath) + E.set_input_file(self.noise_filepath) E.append_effect_to_chain("dcshift", [shift]) sox_output_waveform, sr = E.sox_build_flow_effects() - waveform, _ = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.dcshift(waveform, shift) + output_waveform = F.dcshift(self.noise_waveform, shift) self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @@ -349,14 +333,13 @@ def test_overdrive(self): """ gain = 30 colour = 40 - noise_filepath = common_utils.get_asset_path('whitenoise.wav') + E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(noise_filepath) + E.set_input_file(self.noise_filepath) E.append_effect_to_chain("overdrive", [gain, colour]) sox_output_waveform, sr = E.sox_build_flow_effects() - waveform, _ = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.overdrive(waveform, gain, colour) + output_waveform = F.overdrive(self.noise_waveform, gain, colour) self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @@ -369,14 +352,14 @@ def test_phaser_sine(self): delay_ms = 2.0 decay = 0.4 speed = 0.5 - noise_filepath = common_utils.get_asset_path('whitenoise.wav') + E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(noise_filepath) + E.set_input_file(self.noise_filepath) E.append_effect_to_chain("phaser", [gain_in, gain_out, delay_ms, decay, speed, "-s"]) sox_output_waveform, sr = E.sox_build_flow_effects() - waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.phaser(waveform, sample_rate, gain_in, gain_out, delay_ms, decay, speed, sinusoidal=True) + output_waveform = F.phaser(self.noise_waveform, self.NOISE_SAMPLE_RATE, + gain_in, gain_out, delay_ms, decay, speed, sinusoidal=True) self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @@ -389,14 +372,14 @@ def test_phaser_triangle(self): delay_ms = 2.0 decay = 0.4 speed = 0.5 - noise_filepath = common_utils.get_asset_path('whitenoise.wav') + E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(noise_filepath) + E.set_input_file(self.noise_filepath) E.append_effect_to_chain("phaser", [gain_in, gain_out, delay_ms, decay, speed, "-t"]) sox_output_waveform, sr = E.sox_build_flow_effects() - waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.phaser(waveform, sample_rate, gain_in, gain_out, delay_ms, decay, speed, sinusoidal=False) + output_waveform = F.phaser(self.noise_waveform, self.NOISE_SAMPLE_RATE, + gain_in, gain_out, delay_ms, decay, speed, sinusoidal=False) self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @@ -410,15 +393,14 @@ def test_flanger_triangle_linear(self): width = 0.9 speed = 0.5 phase = 30 - noise_filepath = common_utils.get_asset_path('whitenoise.wav') + E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(noise_filepath) + E.set_input_file(self.noise_filepath) E.append_effect_to_chain("flanger", [delay, depth, regen, width, speed, "triangle", phase, "linear"]) sox_output_waveform, sr = E.sox_build_flow_effects() - waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.flanger(waveform, sample_rate, delay, depth, regen, width, speed, phase, - modulation='triangular', interpolation='linear') + output_waveform = F.flanger(self.noise_waveform, self.NOISE_SAMPLE_RATE, delay, depth, regen, + width, speed, phase, modulation='triangular', interpolation='linear') torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @@ -432,15 +414,14 @@ def test_flanger_triangle_quad(self): width = 0.4 speed = 0.5 phase = 40 - noise_filepath = common_utils.get_asset_path('whitenoise.wav') + E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(noise_filepath) + E.set_input_file(self.noise_filepath) E.append_effect_to_chain("flanger", [delay, depth, regen, width, speed, "triangle", phase, "quadratic"]) sox_output_waveform, sr = E.sox_build_flow_effects() - waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.flanger(waveform, sample_rate, delay, depth, regen, width, speed, phase, - modulation='triangular', interpolation='quadratic') + output_waveform = F.flanger(self.noise_waveform, self.NOISE_SAMPLE_RATE, delay, depth, + regen, width, speed, phase, modulation='triangular', interpolation='quadratic') torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @@ -454,15 +435,14 @@ def test_flanger_sine_linear(self): width = 0.23 speed = 1.3 phase = 60 - noise_filepath = common_utils.get_asset_path('whitenoise.wav') + E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(noise_filepath) + E.set_input_file(self.noise_filepath) E.append_effect_to_chain("flanger", [delay, depth, regen, width, speed, "sine", phase, "linear"]) sox_output_waveform, sr = E.sox_build_flow_effects() - waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.flanger(waveform, sample_rate, delay, depth, regen, width, speed, phase, - modulation='sinusoidal', interpolation='linear') + output_waveform = F.flanger(self.noise_waveform, self.NOISE_SAMPLE_RATE, delay, depth, + regen, width, speed, phase, modulation='sinusoidal', interpolation='linear') torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @@ -476,15 +456,14 @@ def test_flanger_sine_quad(self): width = 0.23 speed = 1.3 phase = 25 - noise_filepath = common_utils.get_asset_path('whitenoise.wav') + E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(noise_filepath) + E.set_input_file(self.noise_filepath) E.append_effect_to_chain("flanger", [delay, depth, regen, width, speed, "sine", phase, "quadratic"]) sox_output_waveform, sr = E.sox_build_flow_effects() - waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.flanger(waveform, sample_rate, delay, depth, regen, width, speed, phase, - modulation='sinusoidal', interpolation='quadratic') + output_waveform = F.flanger(self.noise_waveform, self.NOISE_SAMPLE_RATE, delay, depth, + regen, width, speed, phase, modulation='sinusoidal', interpolation='quadratic') torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) @@ -497,21 +476,17 @@ def test_equalizer(self): q = 0.707 gain = 1 - noise_filepath = common_utils.get_asset_path('whitenoise.wav') E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(noise_filepath) + E.set_input_file(self.noise_filepath) E.append_effect_to_chain("equalizer", [center_freq, q, gain]) sox_output_waveform, sr = E.sox_build_flow_effects() - waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True) - output_waveform = F.equalizer_biquad(waveform, sample_rate, center_freq, gain, q) + output_waveform = F.equalizer_biquad(self.noise_waveform, self.NOISE_SAMPLE_RATE, center_freq, gain, q) self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) def test_perf_biquad_filtering(self): - fn_sine = common_utils.get_asset_path('whitenoise.wav') - b0 = 0.4 b1 = 0.2 b2 = 0.9 @@ -521,13 +496,12 @@ def test_perf_biquad_filtering(self): # SoX method E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(fn_sine) + E.set_input_file(self.noise_filepath) E.append_effect_to_chain("biquad", [b0, b1, b2, a0, a1, a2]) waveform_sox_out, _ = E.sox_build_flow_effects() - waveform, _ = torchaudio.load(fn_sine, normalization=True) waveform_lfilter_out = F.lfilter( - waveform, torch.tensor([a0, a1, a2]), torch.tensor([b0, b1, b2]) + self.noise_waveform, torch.tensor([a0, a1, a2]), torch.tensor([b0, b1, b2]) ) self.assertEqual(waveform_lfilter_out, waveform_sox_out, atol=1e-4, rtol=1e-5) diff --git a/test/test_sox_effects.py b/test/test_sox_effects.py index 68440db8d2..6b12d7cd06 100644 --- a/test/test_sox_effects.py +++ b/test/test_sox_effects.py @@ -1,3 +1,4 @@ +import sys import math import unittest @@ -11,7 +12,7 @@ class Test_SoxEffectsChain(common_utils.TorchaudioTestCase): backend = 'sox' - test_filepath = common_utils.get_asset_path("steam-train-whistle-daniel_simon.mp3") + test_filepath = common_utils.get_asset_path("steam-train-whistle-daniel_simon.wav") def test_single_channel(self): fn_sine = common_utils.get_asset_path("sinewave.wav") @@ -34,6 +35,7 @@ def test_rate_channels(self): self.assertEqual(sr, target_rate) self.assertEqual(x.size(0), target_channels) + @unittest.skipIf(sys.platform == 'darwin', 'This test is known to fail on macOS') def test_lowpass_speed(self): speed = .8 si, _ = torchaudio.info(self.test_filepath) diff --git a/test/test_transforms.py b/test/test_transforms.py index af2b834fdb..6df9562bc8 100644 --- a/test/test_transforms.py +++ b/test/test_transforms.py @@ -45,7 +45,7 @@ def test_mu_law_companding(self): def test_AmplitudeToDB(self): filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') - waveform, sample_rate = torchaudio.load(filepath) + waveform = common_utils.load_wav(filepath)[0] mag_to_db_transform = transforms.AmplitudeToDB('magnitude', 80.) power_to_db_transform = transforms.AmplitudeToDB('power', 80.) @@ -115,7 +115,7 @@ def test_mel2(self): self.assertTrue(mel_transform2.mel_scale.fb.sum(1).ge(0.).all()) # check on multi-channel audio filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') - x_stereo, sr_stereo = torchaudio.load(filepath) # (2, 278756), 44100 + x_stereo = common_utils.load_wav(filepath)[0] # (2, 278756), 44100 spectrogram_stereo = s2db(mel_transform(x_stereo)) # (2, 128, 1394) self.assertTrue(spectrogram_stereo.dim() == 3) self.assertTrue(spectrogram_stereo.size(0) == 2) @@ -166,7 +166,7 @@ def test_mfcc(self): def test_resample_size(self): input_path = common_utils.get_asset_path('sinewave.wav') - waveform, sample_rate = torchaudio.load(input_path) + waveform, sample_rate = common_utils.load_wav(input_path) upsample_rate = sample_rate * 2 downsample_rate = sample_rate // 2 diff --git a/test/torchscript_consistency_impl.py b/test/torchscript_consistency_impl.py index 2527cd5fbf..76444dc18a 100644 --- a/test/torchscript_consistency_impl.py +++ b/test/torchscript_consistency_impl.py @@ -2,7 +2,6 @@ import unittest import torch -import torchaudio import torchaudio.functional as F import torchaudio.transforms as T @@ -616,6 +615,5 @@ def test_SlidingWindowCmn(self): def test_Vad(self): filepath = common_utils.get_asset_path("vad-go-mono-32000.wav") - common_utils.set_audio_backend('default') - waveform, sample_rate = torchaudio.load(filepath) + waveform, sample_rate = common_utils.load_wav(filepath) self._assert_consistency(T.Vad(sample_rate=sample_rate), waveform) diff --git a/third_party/CMakeLists.txt b/third_party/CMakeLists.txt new file mode 100644 index 0000000000..c4be4cb7b0 --- /dev/null +++ b/third_party/CMakeLists.txt @@ -0,0 +1,80 @@ +cmake_minimum_required(VERSION 3.1) + +project(torchaudio_third_parties) +include(ExternalProject) + +set(INSTALL_DIR ${CMAKE_CURRENT_SOURCE_DIR}/install) +set(ARCHIVE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/archives) +set(COMMON_ARGS --quiet --disable-shared --enable-static --prefix=${INSTALL_DIR} --with-pic --disable-dependency-tracking --disable-debug --disable-examples --disable-doc) + +ExternalProject_Add(libmad + PREFIX ${CMAKE_CURRENT_SOURCE_DIR} + DOWNLOAD_DIR ${ARCHIVE_DIR} + URL https://downloads.sourceforge.net/project/mad/libmad/0.15.1b/libmad-0.15.1b.tar.gz + URL_HASH SHA256=bbfac3ed6bfbc2823d3775ebb931087371e142bb0e9bb1bee51a76a6e0078690 + PATCH_COMMAND patch < ${CMAKE_CURRENT_SOURCE_DIR}/patch/libmad.patch + CONFIGURE_COMMAND ${CMAKE_CURRENT_SOURCE_DIR}/src/libmad/configure ${COMMON_ARGS} +) + +ExternalProject_Add(libmp3lame + PREFIX ${CMAKE_CURRENT_SOURCE_DIR} + DOWNLOAD_DIR ${ARCHIVE_DIR} + URL https://downloads.sourceforge.net/project/lame/lame/3.99/lame-3.99.5.tar.gz + URL_HASH SHA256=24346b4158e4af3bd9f2e194bb23eb473c75fb7377011523353196b19b9a23ff + CONFIGURE_COMMAND ${CMAKE_CURRENT_SOURCE_DIR}/src/libmp3lame/configure ${COMMON_ARGS} --enable-nasm +) + +ExternalProject_Add(libogg + PREFIX ${CMAKE_CURRENT_SOURCE_DIR} + DOWNLOAD_DIR ${ARCHIVE_DIR} + URL https://ftp.osuosl.org/pub/xiph/releases/ogg/libogg-1.3.3.tar.gz + URL_HASH SHA256=c2e8a485110b97550f453226ec644ebac6cb29d1caef2902c007edab4308d985 + CONFIGURE_COMMAND ${CMAKE_CURRENT_SOURCE_DIR}/src/libogg/configure ${COMMON_ARGS} +) + +ExternalProject_Add(libflac + PREFIX ${CMAKE_CURRENT_SOURCE_DIR} + DEPENDS libogg + DOWNLOAD_DIR ${ARCHIVE_DIR} + URL https://ftp.osuosl.org/pub/xiph/releases/flac/flac-1.3.2.tar.xz + URL_HASH SHA256=91cfc3ed61dc40f47f050a109b08610667d73477af6ef36dcad31c31a4a8d53f + CONFIGURE_COMMAND ${CMAKE_CURRENT_SOURCE_DIR}/build_codec_helper.sh ${CMAKE_CURRENT_SOURCE_DIR}/src/libflac/configure ${COMMON_ARGS} --with-ogg +) + +ExternalProject_Add(libvorbis + PREFIX ${CMAKE_CURRENT_SOURCE_DIR} + DEPENDS libogg + DOWNLOAD_DIR ${ARCHIVE_DIR} + URL https://ftp.osuosl.org/pub/xiph/releases/vorbis/libvorbis-1.3.6.tar.gz + URL_HASH SHA256=6ed40e0241089a42c48604dc00e362beee00036af2d8b3f46338031c9e0351cb + CONFIGURE_COMMAND ${CMAKE_CURRENT_SOURCE_DIR}/build_codec_helper.sh ${CMAKE_CURRENT_SOURCE_DIR}/src/libvorbis/configure ${COMMON_ARGS} --with-ogg +) + +ExternalProject_Add(libopus + PREFIX ${CMAKE_CURRENT_SOURCE_DIR} + DEPENDS libogg + DOWNLOAD_DIR ${ARCHIVE_DIR} + URL https://ftp.osuosl.org/pub/xiph/releases/opus/opus-1.3.1.tar.gz + URL_HASH SHA256=65b58e1e25b2a114157014736a3d9dfeaad8d41be1c8179866f144a2fb44ff9d + CONFIGURE_COMMAND ${CMAKE_CURRENT_SOURCE_DIR}/build_codec_helper.sh ${CMAKE_CURRENT_SOURCE_DIR}/src/libopus/configure ${COMMON_ARGS} --with-ogg +) + +ExternalProject_Add(opusfile + PREFIX ${CMAKE_CURRENT_SOURCE_DIR} + DEPENDS libopus + DOWNLOAD_DIR ${ARCHIVE_DIR} + STAMP_DIR ${CMAKE_CURRENT_SOURCE_DIR}/src/opusfile-stamp + SOURCE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/src/opusfile + URL https://ftp.osuosl.org/pub/xiph/releases/opus/opusfile-0.12.tar.gz + URL_HASH SHA256=118d8601c12dd6a44f52423e68ca9083cc9f2bfe72da7a8c1acb22a80ae3550b + CONFIGURE_COMMAND ${CMAKE_CURRENT_SOURCE_DIR}/build_codec_helper.sh ${CMAKE_CURRENT_SOURCE_DIR}/src/opusfile/configure ${COMMON_ARGS} --disable-http +) + +ExternalProject_Add(libsox + PREFIX ${CMAKE_CURRENT_SOURCE_DIR} + DEPENDS libogg libflac libvorbis opusfile libmp3lame libmad + DOWNLOAD_DIR ${ARCHIVE_DIR} + URL https://downloads.sourceforge.net/project/sox/sox/14.4.2/sox-14.4.2.tar.bz2 + URL_HASH SHA256=81a6956d4330e75b5827316e44ae381e6f1e8928003c6aa45896da9041ea149c + CONFIGURE_COMMAND ${CMAKE_CURRENT_SOURCE_DIR}/build_codec_helper.sh ${CMAKE_CURRENT_SOURCE_DIR}/src/libsox/configure ${COMMON_ARGS} --with-lame --with-flac --with-mad --with-oggvorbis --without-alsa --without-coreaudio --without-png --without-oss --without-sndfile --with-opus +) diff --git a/third_party/build_codec_helper.sh b/third_party/build_codec_helper.sh new file mode 100755 index 0000000000..e7f2614781 --- /dev/null +++ b/third_party/build_codec_helper.sh @@ -0,0 +1,13 @@ +#!/usr/bin/env bash +# Helper script for building codecs depending on libogg, such as libopus and opus. +# It is difficult to set environment variable inside of ExternalProject_Add, +# so this script sets necessary environment variables before running the given command + +this_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" +install_dir="${this_dir}/install" + +export PKG_CONFIG_PATH="${install_dir}/lib/pkgconfig" +export LDFLAGS="-L${install_dir}/lib ${LDFLAGS}" +export CPPFLAGS="-I${install_dir}/include ${CPPFLAGS}" + +$@ diff --git a/third_party/patch/libmad.patch b/third_party/patch/libmad.patch new file mode 100644 index 0000000000..a805787831 --- /dev/null +++ b/third_party/patch/libmad.patch @@ -0,0 +1,86 @@ +See the followings for the origin of this patch +http://www.linuxfromscratch.org/blfs/view/svn/multimedia/libmad.html +http://www.linuxfromscratch.org/patches/blfs/svn/libmad-0.15.1b-fixes-1.patch +--- src/libmad/configure 2004-02-05 09:34:07.000000000 +0000 ++++ src/libmad/configure.new 2020-06-30 21:10:28.528018931 +0000 +@@ -19083,71 +19083,7 @@ + + if test "$GCC" = yes + then +- if test -z "$arch" +- then +- case "$host" in +- i386-*) ;; +- i?86-*) arch="-march=i486" ;; +- arm*-empeg-*) arch="-march=armv4 -mtune=strongarm1100" ;; +- armv4*-*) arch="-march=armv4 -mtune=strongarm" ;; +- powerpc-*) ;; +- mips*-agenda-*) arch="-mcpu=vr4100" ;; +- mips*-luxsonor-*) arch="-mips1 -mcpu=r3000 -Wa,-m4010" ;; +- esac +- fi +- +- case "$optimize" in +- -O|"-O "*) +- optimize="-O" +- optimize="$optimize -fforce-mem" +- optimize="$optimize -fforce-addr" +- : #x optimize="$optimize -finline-functions" +- : #- optimize="$optimize -fstrength-reduce" +- optimize="$optimize -fthread-jumps" +- optimize="$optimize -fcse-follow-jumps" +- optimize="$optimize -fcse-skip-blocks" +- : #x optimize="$optimize -frerun-cse-after-loop" +- : #x optimize="$optimize -frerun-loop-opt" +- : #x optimize="$optimize -fgcse" +- optimize="$optimize -fexpensive-optimizations" +- optimize="$optimize -fregmove" +- : #* optimize="$optimize -fdelayed-branch" +- : #x optimize="$optimize -fschedule-insns" +- optimize="$optimize -fschedule-insns2" +- : #? optimize="$optimize -ffunction-sections" +- : #? optimize="$optimize -fcaller-saves" +- : #> optimize="$optimize -funroll-loops" +- : #> optimize="$optimize -funroll-all-loops" +- : #x optimize="$optimize -fmove-all-movables" +- : #x optimize="$optimize -freduce-all-givs" +- : #? optimize="$optimize -fstrict-aliasing" +- : #* optimize="$optimize -fstructure-noalias" +- +- case "$host" in +- arm*-*) +- optimize="$optimize -fstrength-reduce" +- ;; +- mips*-*) +- optimize="$optimize -fstrength-reduce" +- optimize="$optimize -finline-functions" +- ;; +- i?86-*) +- optimize="$optimize -fstrength-reduce" +- ;; +- powerpc-apple-*) +- # this triggers an internal compiler error with gcc2 +- : #optimize="$optimize -fstrength-reduce" +- +- # this is really only beneficial with gcc3 +- : #optimize="$optimize -finline-functions" +- ;; +- *) +- # this sometimes provokes bugs in gcc 2.95.2 +- : #optimize="$optimize -fstrength-reduce" +- ;; +- esac +- ;; +- esac ++ optimize="-O2" + fi + + case "$host" in +@@ -21497,6 +21433,7 @@ + then + case "$host" in + i?86-*) FPM="INTEL" ;; ++ x86_64*) FPM="64BIT" ;; + arm*-*) FPM="ARM" ;; + mips*-*) FPM="MIPS" ;; + sparc*-*) FPM="SPARC" ;; diff --git a/torchaudio/backend/sox_io_backend.py b/torchaudio/backend/sox_io_backend.py new file mode 100644 index 0000000000..4664f733c6 --- /dev/null +++ b/torchaudio/backend/sox_io_backend.py @@ -0,0 +1,134 @@ +from typing import Tuple, Optional + +import torch +from torchaudio._internal import ( + module_utils as _mod_utils, +) + + +class AudioMetaData: + def __init__(self, sample_rate: int, num_frames: int, num_channels: int): + self.sample_rate = sample_rate + self.num_frames = num_frames + self.num_channels = num_channels + + +@_mod_utils.requires_module('torchaudio._torchaudio') +def info(filepath: str) -> AudioMetaData: + """Get signal information of an audio file.""" + sinfo = torch.ops.torchaudio.sox_io_get_info(filepath) + return AudioMetaData(sinfo.get_sample_rate(), sinfo.get_num_frames(), sinfo.get_num_channels()) + + +@_mod_utils.requires_module('torchaudio._torchaudio') +def load( + filepath: str, + frame_offset: int = 0, + num_frames: int = -1, + normalize: bool = True, + channels_first: bool = True, +) -> Tuple[torch.Tensor, int]: + """Load audio data from file. + + This function can handle all the codecs that underlying libsox can handle, however note the + followings. + + Note: + This function is tested on the following formats; + - WAV + - 32-bit floating-point + - 32-bit signed integer + - 16-bit signed integer + - 8-bit unsigned integer + - MP3 + - FLAC + - OGG/VORBIS + + By default, this function returns Tensor with ``float32`` dtype and the shape of ``[channel, time]``. + The samples are normalized to fit in the range of ``[-1.0, 1.0]``. + + When the input format is WAV with integer type, such as 32-bit signed integer, 16-bit + signed integer and 8-bit unsigned integer (24-bit signed integer is not supported), + by providing ``normalize=False``, this function can return integer Tensor, where the samples + are expressed within the whole range of the corresponding dtype, that is, ``int32`` tensor + for 32-bit signed PCM, ``int16`` for 16-bit signed PCM and ``uint8`` for 8-bit unsigned PCM. + + ``normalize`` parameter has no effect on 32-bit floating-point WAV and other formats, such as + flac and mp3. For these formats, this function always returns ``float32`` Tensor with values + normalized to ``[-1.0, 1.0]``. + + Args: + filepath: Path to audio file + frame_offset: Number of frames to skip before start reading data. + num_frames: Maximum number of frames to read. -1 reads all the remaining samples, starting + from ``frame_offset``. This function may return the less number of frames if there is + not enough frames in the given file. + normalize: When ``True``, this function always return ``float32``, and sample values are + normalized to ``[-1.0, 1.0]``. If input file is integer WAV, giving ``False`` will change + the resulting Tensor type to integer type. This argument has no effect for formats other + than integer WAV type. + channels_first: When True, the returned Tensor has dimension ``[channel, time]``. + Otherwise, the returned Tensor's dimension is ``[time, channel]``. + + Returns: + torch.Tensor: If the input file has integer wav format and normalization is off, then it has + integer type, else ``float32`` type. If ``channels_first=True``, it has + ``[channel, time]`` else ``[time, channel]``. + """ + signal = torch.ops.torchaudio.sox_io_load_audio_file( + filepath, frame_offset, num_frames, normalize, channels_first) + return signal.get_tensor(), signal.get_sample_rate() + + +@_mod_utils.requires_module('torchaudio._torchaudio') +def save( + filepath: str, + tensor: torch.Tensor, + sample_rate: int, + channels_first: bool = True, + compression: Optional[float] = None, +): + """Save audio data to file. + + Supported formats are; + - WAV + - 32-bit floating-point + - 32-bit signed integer + - 16-bit signed integer + - 8-bit unsigned integer + - MP3 + - FLAC + - OGG/VORBIS + + Args: + filepath: Path to save file. + tensor: Audio data to save. must be 2D tensor. + sample_rate: sampling rate + channels_first: If True, the given tensor is interpreted as ``[channel, time]``. + compression: Used for formats other than WAV. This corresponds to ``-C`` option + of ``sox`` command. + See the detail at http://sox.sourceforge.net/soxformat.html. + - MP3: Either bitrate [kbps] with quality factor, such as ``128.2`` or + VBR encoding with quality factor such as ``-4.2``. Default: ``-4.5`` + - FLAC: compression level. Whole number from ``0`` to ``8``. + ``8`` is default and highest compression. + - OGG/VORBIS: number from -1 to 10; -1 is the highest compression and lowest + quality. Default: ``3``. + """ + if compression is None: + ext = str(filepath)[-3:].lower() + if ext == 'wav': + compression = 0. + elif ext == 'mp3': + compression = -4.5 + elif ext == 'flac': + compression = 8. + elif ext in ['ogg', 'vorbis']: + compression = 3. + else: + raise RuntimeError(f'Unsupported file type: "{ext}"') + signal = torch.classes.torchaudio.TensorSignal(tensor, sample_rate, channels_first) + torch.ops.torchaudio.sox_io_save_audio_file(filepath, signal, compression) + + +load_wav = load diff --git a/torchaudio/backend/utils.py b/torchaudio/backend/utils.py index d537f01daf..cb53b3e02f 100644 --- a/torchaudio/backend/utils.py +++ b/torchaudio/backend/utils.py @@ -7,6 +7,7 @@ from . import ( no_backend, sox_backend, + sox_io_backend, soundfile_backend, ) @@ -24,6 +25,7 @@ def list_audio_backends() -> List[str]: backends.append('soundfile') if is_module_available('torchaudio._torchaudio'): backends.append('sox') + backends.append('sox_io') return backends @@ -43,6 +45,8 @@ def set_audio_backend(backend: Optional[str]) -> None: module = no_backend elif backend == 'sox': module = sox_backend + elif backend == 'sox_io': + module = sox_io_backend elif backend == 'soundfile': module = soundfile_backend else: @@ -69,6 +73,8 @@ def get_audio_backend() -> Optional[str]: return None if torchaudio.load == sox_backend.load: return 'sox' + if torchaudio.load == sox_io_backend.load: + return 'sox_io' if torchaudio.load == soundfile_backend.load: return 'soundfile' raise ValueError('Unknown backend.') diff --git a/torchaudio/csrc/register.cpp b/torchaudio/csrc/register.cpp index 81b1a84c96..3c03232941 100644 --- a/torchaudio/csrc/register.cpp +++ b/torchaudio/csrc/register.cpp @@ -1,17 +1,64 @@ #ifndef TORCHAUDIO_REGISTER_H #define TORCHAUDIO_REGISTER_H -#include +#include +#include +#include namespace torchaudio { namespace { +//////////////////////////////////////////////////////////////////////////////// +// sox_utils.h +//////////////////////////////////////////////////////////////////////////////// +static auto registerTensorSignal = + torch::class_("torchaudio", "TensorSignal") + .def(torch::init()) + .def("get_tensor", &sox_utils::TensorSignal::getTensor) + .def("get_sample_rate", &sox_utils::TensorSignal::getSampleRate) + .def("get_channels_first", &sox_utils::TensorSignal::getChannelsFirst); + +//////////////////////////////////////////////////////////////////////////////// +// sox_io.h +//////////////////////////////////////////////////////////////////////////////// static auto registerSignalInfo = - torch::class_("torchaudio", "SignalInfo") - .def(torch::init()) - .def("get_sample_rate", &SignalInfo::getSampleRate) - .def("get_num_channels", &SignalInfo::getNumChannels) - .def("get_num_samples", &SignalInfo::getNumSamples); + torch::class_("torchaudio", "SignalInfo") + .def("get_sample_rate", &sox_io::SignalInfo::getSampleRate) + .def("get_num_channels", &sox_io::SignalInfo::getNumChannels) + .def("get_num_frames", &sox_io::SignalInfo::getNumFrames); + +static auto registerGetInfo = torch::RegisterOperators().op( + torch::RegisterOperators::options() + .schema( + "torchaudio::sox_io_get_info(str path) -> __torch__.torch.classes.torchaudio.SignalInfo info") + .catchAllKernel()); + +static auto registerLoadAudioFile = torch::RegisterOperators().op( + torch::RegisterOperators::options() + .schema( + "torchaudio::sox_io_load_audio_file(str path, int frame_offset, int num_frames, bool normalize, bool channels_first) -> __torch__.torch.classes.torchaudio.TensorSignal signal") + .catchAllKernel< + decltype(sox_io::load_audio_file), + &sox_io::load_audio_file>()); + +static auto registerSaveAudioFile = torch::RegisterOperators().op( + torch::RegisterOperators::options() + .schema( + "torchaudio::sox_io_save_audio_file(str path, __torch__.torch.classes.torchaudio.TensorSignal signal, float compression) -> ()") + .catchAllKernel< + decltype(sox_io::save_audio_file), + &sox_io::save_audio_file>()); + +//////////////////////////////////////////////////////////////////////////////// +// sox_effects.h +//////////////////////////////////////////////////////////////////////////////// +static auto registerSoxEffects = + torch::RegisterOperators( + "torchaudio::sox_effects_initialize_sox_effects", + &sox_effects::initialize_sox_effects) + .op("torchaudio::sox_effects_shutdown_sox_effects", + &sox_effects::shutdown_sox_effects) + .op("torchaudio::sox_effects_list_effects", &sox_effects::list_effects); } // namespace } // namespace torchaudio diff --git a/torchaudio/csrc/sox.cpp b/torchaudio/csrc/sox.cpp index 3ae81bef19..0f099946fd 100644 --- a/torchaudio/csrc/sox.cpp +++ b/torchaudio/csrc/sox.cpp @@ -82,17 +82,6 @@ std::tuple get_info( return std::make_tuple(fd->signal, fd->encoding); } -std::vector get_effect_names() { - sox_effect_fn_t const * fns = sox_get_effect_fns(); - std::vector sv; - for(int i = 0; fns[i]; ++i) { - const sox_effect_handler_t *eh = fns[i] (); - if(eh && eh->name) - sv.push_back(eh->name); - } - return sv; -} - int read_audio_file( const std::string& file_name, at::Tensor output, @@ -186,16 +175,6 @@ void write_audio_file( } } -int initialize_sox() { - /* Initialization for sox effects. Only initialize once */ - return sox_init(); -} - -int shutdown_sox() { - /* Shutdown for sox effects. Do not shutdown between multiple calls */ - return sox_quit(); -} - int build_flow_effects(const std::string& file_name, at::Tensor otensor, bool ch_first, @@ -489,20 +468,8 @@ PYBIND11_MODULE(_torchaudio, m) { "get_info", &torch::audio::get_info, "Gets information about an audio file"); - m.def( - "get_effect_names", - &torch::audio::get_effect_names, - "Gets the names of all available effects"); m.def( "build_flow_effects", &torch::audio::build_flow_effects, "build effects and flow chain into tensors"); - m.def( - "initialize_sox", - &torch::audio::initialize_sox, - "initialize sox for effects"); - m.def( - "shutdown_sox", - &torch::audio::shutdown_sox, - "shutdown sox for effects"); } diff --git a/torchaudio/csrc/sox.h b/torchaudio/csrc/sox.h index 8d851c9b21..8093f0732e 100644 --- a/torchaudio/csrc/sox.h +++ b/torchaudio/csrc/sox.h @@ -45,13 +45,6 @@ void write_audio_file( std::tuple get_info( const std::string& file_name); -// get names of all sox effects -std::vector get_effect_names(); - -// Initialize and Shutdown SoX effects chain. These functions should only be run once. -int initialize_sox(); -int shutdown_sox(); - // Struct for build_flow_effects function struct SoxEffect { SoxEffect() : ename(""), eopts({""}) { } diff --git a/torchaudio/csrc/sox_effects.cpp b/torchaudio/csrc/sox_effects.cpp new file mode 100644 index 0000000000..9a0c2ddc6f --- /dev/null +++ b/torchaudio/csrc/sox_effects.cpp @@ -0,0 +1,54 @@ +#include +#include + +using namespace torch::indexing; + +namespace torchaudio { +namespace sox_effects { + +namespace { + +enum SoxEffectsResourceState { NotInitialized, Initialized, ShutDown }; +SoxEffectsResourceState SOX_RESOURCE_STATE = NotInitialized; + +} // namespace + +void initialize_sox_effects() { + if (SOX_RESOURCE_STATE == ShutDown) { + throw std::runtime_error( + "SoX Effects has been shut down. Cannot initialize again."); + } + if (SOX_RESOURCE_STATE == NotInitialized) { + if (sox_init() != SOX_SUCCESS) { + throw std::runtime_error("Failed to initialize sox effects."); + }; + SOX_RESOURCE_STATE = Initialized; + } +}; + +void shutdown_sox_effects() { + if (SOX_RESOURCE_STATE == NotInitialized) { + throw std::runtime_error( + "SoX Effects is not initialized. Cannot shutdown."); + } + if (SOX_RESOURCE_STATE == Initialized) { + if (sox_quit() != SOX_SUCCESS) { + throw std::runtime_error("Failed to initialize sox effects."); + }; + SOX_RESOURCE_STATE = ShutDown; + } +} + +std::vector list_effects() { + std::vector names; + const sox_effect_fn_t* fns = sox_get_effect_fns(); + for (int i = 0; fns[i]; ++i) { + const sox_effect_handler_t* handler = fns[i](); + if (handler && handler->name) + names.push_back(handler->name); + } + return names; +} + +} // namespace sox_effects +} // namespace torchaudio diff --git a/torchaudio/csrc/sox_effects.h b/torchaudio/csrc/sox_effects.h new file mode 100644 index 0000000000..14bdbbfabc --- /dev/null +++ b/torchaudio/csrc/sox_effects.h @@ -0,0 +1,18 @@ +#ifndef TORCHAUDIO_SOX_EFFECTS_H +#define TORCHAUDIO_SOX_EFFECTS_H + +#include + +namespace torchaudio { +namespace sox_effects { + +void initialize_sox_effects(); + +void shutdown_sox_effects(); + +std::vector list_effects(); + +} // namespace sox_effects +} // namespace torchaudio + +#endif diff --git a/torchaudio/csrc/sox_io.cpp b/torchaudio/csrc/sox_io.cpp new file mode 100644 index 0000000000..5d308027bb --- /dev/null +++ b/torchaudio/csrc/sox_io.cpp @@ -0,0 +1,170 @@ +#include +#include +#include + +using namespace torch::indexing; +using namespace torchaudio::sox_utils; + +namespace torchaudio { +namespace sox_io { + +SignalInfo::SignalInfo( + const int64_t sample_rate_, + const int64_t num_channels_, + const int64_t num_frames_) + : sample_rate(sample_rate_), + num_channels(num_channels_), + num_frames(num_frames_){}; + +int64_t SignalInfo::getSampleRate() const { + return sample_rate; +} + +int64_t SignalInfo::getNumChannels() const { + return num_channels; +} + +int64_t SignalInfo::getNumFrames() const { + return num_frames; +} + +c10::intrusive_ptr get_info(const std::string& path) { + SoxFormat sf(sox_open_read( + path.c_str(), + /*signal=*/nullptr, + /*encoding=*/nullptr, + /*filetype=*/nullptr)); + + if (static_cast(sf) == nullptr) { + throw std::runtime_error("Error opening audio file"); + } + + return c10::make_intrusive( + static_cast(sf->signal.rate), + static_cast(sf->signal.channels), + static_cast(sf->signal.length / sf->signal.channels)); +} + +c10::intrusive_ptr load_audio_file( + const std::string& path, + const int64_t frame_offset, + const int64_t num_frames, + const bool normalize, + const bool channels_first) { + if (frame_offset < 0) { + throw std::runtime_error( + "Invalid argument: frame_offset must be non-negative."); + } + if (num_frames == 0 || num_frames < -1) { + throw std::runtime_error( + "Invalid argument: num_frames must be -1 or greater than 0."); + } + + SoxFormat sf(sox_open_read( + path.c_str(), + /*signal=*/nullptr, + /*encoding=*/nullptr, + /*filetype=*/nullptr)); + + validate_input_file(sf); + + const int64_t num_channels = sf->signal.channels; + const int64_t num_total_samples = sf->signal.length; + const int64_t sample_start = sf->signal.channels * frame_offset; + + if (sox_seek(sf, sample_start, 0) == SOX_EOF) { + throw std::runtime_error("Error reading audio file: offset past EOF."); + } + + const int64_t sample_end = [&]() { + if (num_frames == -1) + return num_total_samples; + const int64_t sample_end_ = num_channels * num_frames + sample_start; + if (num_total_samples < sample_end_) { + // For lossy encoding, it is difficult to predict exact size of buffer for + // reading the number of samples required. + // So we allocate buffer size of given `num_frames` and ask sox to read as + // much as possible. For lossless format, sox reads exact number of + // samples, but for lossy encoding, sox can end up reading less. (i.e. + // mp3) For the consistent behavior specification between lossy/lossless + // format, we allow users to provide `num_frames` value that exceeds #of + // available samples, and we adjust it here. + return num_total_samples; + } + return sample_end_; + }(); + + const int64_t max_samples = sample_end - sample_start; + + // Read samples into buffer + std::vector buffer; + buffer.reserve(max_samples); + const int64_t num_samples = sox_read(sf, buffer.data(), max_samples); + if (num_samples == 0) { + throw std::runtime_error( + "Error reading audio file: empty file or read operation failed."); + } + // NOTE: num_samples may be smaller than max_samples if the input + // format is compressed (i.e. mp3). + + // Convert to Tensor + auto tensor = convert_to_tensor( + buffer.data(), + num_samples, + num_channels, + get_dtype(sf->encoding.encoding, sf->signal.precision), + normalize, + channels_first); + + return c10::make_intrusive( + tensor, static_cast(sf->signal.rate), channels_first); +} + +void save_audio_file( + const std::string& file_name, + const c10::intrusive_ptr& signal, + const double compression) { + const auto tensor = signal->getTensor(); + const auto sample_rate = signal->getSampleRate(); + const auto channels_first = signal->getChannelsFirst(); + + validate_input_tensor(tensor); + + const auto filetype = get_filetype(file_name); + const auto signal_info = + get_signalinfo(tensor, sample_rate, channels_first, filetype); + const auto encoding_info = + get_encodinginfo(filetype, tensor.dtype(), compression); + + SoxFormat sf(sox_open_write( + file_name.c_str(), + &signal_info, + &encoding_info, + /*filetype=*/filetype.c_str(), + /*oob=*/nullptr, + /*overwrite_permitted=*/nullptr)); + + if (static_cast(sf) == nullptr) { + throw std::runtime_error("Error saving audio file: failed to open file."); + } + + auto tensor_ = tensor; + if (channels_first) { + tensor_ = tensor_.t(); + } + + const int64_t frames_per_chunk = 65536; + for (int64_t i = 0; i < tensor_.size(0); i += frames_per_chunk) { + auto chunk = tensor_.index({Slice(i, i + frames_per_chunk), Slice()}); + chunk = unnormalize_wav(chunk).contiguous(); + + const size_t numel = chunk.numel(); + if (sox_write(sf, chunk.data_ptr(), numel) != numel) { + throw std::runtime_error( + "Error saving audio file: failed to write the entier buffer."); + } + } +} + +} // namespace sox_io +} // namespace torchaudio diff --git a/torchaudio/csrc/sox_io.h b/torchaudio/csrc/sox_io.h new file mode 100644 index 0000000000..5288e911e8 --- /dev/null +++ b/torchaudio/csrc/sox_io.h @@ -0,0 +1,41 @@ +#ifndef TORCHAUDIO_SOX_IO_H +#define TORCHAUDIO_SOX_IO_H + +#include +#include + +namespace torchaudio { +namespace sox_io { + +struct SignalInfo : torch::CustomClassHolder { + int64_t sample_rate; + int64_t num_channels; + int64_t num_frames; + + SignalInfo( + const int64_t sample_rate_, + const int64_t num_channels_, + const int64_t num_frames_); + int64_t getSampleRate() const; + int64_t getNumChannels() const; + int64_t getNumFrames() const; +}; + +c10::intrusive_ptr get_info(const std::string& path); + +c10::intrusive_ptr load_audio_file( + const std::string& path, + const int64_t frame_offset = 0, + const int64_t num_frames = -1, + const bool normalize = true, + const bool channels_first = true); + +void save_audio_file( + const std::string& file_name, + const c10::intrusive_ptr& signal, + const double compression = 0.); + +} // namespace sox_io +} // namespace torchaudio + +#endif diff --git a/torchaudio/csrc/sox_utils.cpp b/torchaudio/csrc/sox_utils.cpp new file mode 100644 index 0000000000..c1fd8383a8 --- /dev/null +++ b/torchaudio/csrc/sox_utils.cpp @@ -0,0 +1,245 @@ +#include +#include +#include + +namespace torchaudio { +namespace sox_utils { + +TensorSignal::TensorSignal( + torch::Tensor tensor_, + int64_t sample_rate_, + bool channels_first_) + : tensor(tensor_), + sample_rate(sample_rate_), + channels_first(channels_first_){}; + +torch::Tensor TensorSignal::getTensor() const { + return tensor; +} +int64_t TensorSignal::getSampleRate() const { + return sample_rate; +} +bool TensorSignal::getChannelsFirst() const { + return channels_first; +} + +SoxFormat::SoxFormat(sox_format_t* fd) noexcept : fd_(fd) {} +SoxFormat::~SoxFormat() { + if (fd_ != nullptr) { + sox_close(fd_); + } +} +sox_format_t* SoxFormat::operator->() const noexcept { + return fd_; +} +SoxFormat::operator sox_format_t*() const noexcept { + return fd_; +} + +void validate_input_file(const SoxFormat& sf) { + if (static_cast(sf) == nullptr) { + throw std::runtime_error("Error loading audio file: failed to open file."); + } + if (sf->encoding.encoding == SOX_ENCODING_UNKNOWN) { + throw std::runtime_error("Error loading audio file: unknown encoding."); + } + if (sf->signal.length == 0) { + throw std::runtime_error("Error reading audio file: unkown length."); + } +} + +void validate_input_tensor(const torch::Tensor tensor) { + if (!tensor.device().is_cpu()) { + throw std::runtime_error("Input tensor has to be on CPU."); + } + + if (tensor.ndimension() != 2) { + throw std::runtime_error("Input tensor has to be 2D."); + } + + const auto dtype = tensor.dtype(); + if (!(dtype == torch::kFloat32 || dtype == torch::kInt32 || + dtype == torch::kInt16 || dtype == torch::kUInt8)) { + throw std::runtime_error( + "Input tensor has to be one of float32, int32, int16 or uint8 type."); + } +} + +caffe2::TypeMeta get_dtype( + const sox_encoding_t encoding, + const unsigned precision) { + const auto dtype = [&]() { + switch (encoding) { + case SOX_ENCODING_UNSIGNED: // 8-bit PCM WAV + return torch::kUInt8; + case SOX_ENCODING_SIGN2: // 16-bit or 32-bit PCM WAV + switch (precision) { + case 16: + return torch::kInt16; + case 32: + return torch::kInt32; + default: + throw std::runtime_error( + "Only 16 and 32 bits are supported for signed PCM."); + } + default: + // default to float32 for the other formats, including + // 32-bit flaoting-point WAV, + // MP3, + // FLAC, + // VORBIS etc... + return torch::kFloat32; + } + }(); + return c10::scalarTypeToTypeMeta(dtype); +} + +torch::Tensor convert_to_tensor( + sox_sample_t* buffer, + const int32_t num_samples, + const int32_t num_channels, + const caffe2::TypeMeta dtype, + const bool normalize, + const bool channels_first) { + auto t = torch::from_blob( + buffer, {num_samples / num_channels, num_channels}, torch::kInt32); + // Note: Tensor created from_blob does not own data but borrwos + // So make sure to create a new copy after processing samples. + if (normalize || dtype == torch::kFloat32) { + t = t.to(torch::kFloat32); + t *= (t > 0) / 2147483647. + (t < 0) / 2147483648.; + } else if (dtype == torch::kInt32) { + t = t.clone(); + } else if (dtype == torch::kInt16) { + t.floor_divide_(1 << 16); + t = t.to(torch::kInt16); + } else if (dtype == torch::kUInt8) { + t.floor_divide_(1 << 24); + t += 128; + t = t.to(torch::kUInt8); + } else { + throw std::runtime_error("Unsupported dtype."); + } + if (channels_first) { + t = t.transpose(1, 0); + } + return t.contiguous(); +} + +torch::Tensor unnormalize_wav(const torch::Tensor input_tensor) { + const auto dtype = input_tensor.dtype(); + auto tensor = input_tensor; + if (dtype == torch::kFloat32) { + double multi_pos = 2147483647.; + double multi_neg = -2147483648.; + auto mult = (tensor > 0) * multi_pos - (tensor < 0) * multi_neg; + tensor = tensor.to(torch::dtype(torch::kFloat64)); + tensor *= mult; + tensor.clamp_(multi_neg, multi_pos); + tensor = tensor.to(torch::dtype(torch::kInt32)); + } else if (dtype == torch::kInt32) { + // already denormalized + } else if (dtype == torch::kInt16) { + tensor = tensor.to(torch::dtype(torch::kInt32)); + tensor *= ((tensor != 0) * 65536); + } else if (dtype == torch::kUInt8) { + tensor = tensor.to(torch::dtype(torch::kInt32)); + tensor -= 128; + tensor *= 16777216; + } else { + throw std::runtime_error("Unexpected dtype."); + } + return tensor; +} + +const std::string get_filetype(const std::string path) { + std::string ext = path.substr(path.find_last_of(".") + 1); + std::transform(ext.begin(), ext.end(), ext.begin(), ::tolower); + return ext; +} + +sox_encoding_t get_encoding( + const std::string filetype, + const caffe2::TypeMeta dtype) { + if (filetype == "mp3") + return SOX_ENCODING_MP3; + if (filetype == "flac") + return SOX_ENCODING_FLAC; + if (filetype == "ogg" || filetype == "vorbis") + return SOX_ENCODING_VORBIS; + if (filetype == "wav") { + if (dtype == torch::kUInt8) + return SOX_ENCODING_UNSIGNED; + if (dtype == torch::kInt16) + return SOX_ENCODING_SIGN2; + if (dtype == torch::kInt32) + return SOX_ENCODING_SIGN2; + if (dtype == torch::kFloat32) + return SOX_ENCODING_FLOAT; + throw std::runtime_error("Unsupported dtype."); + } + throw std::runtime_error("Unsupported file type."); +} + +unsigned get_precision( + const std::string filetype, + const caffe2::TypeMeta dtype) { + if (filetype == "mp3") + return SOX_UNSPEC; + if (filetype == "flac") + return 24; + if (filetype == "ogg" || filetype == "vorbis") + return SOX_UNSPEC; + if (filetype == "wav") { + if (dtype == torch::kUInt8) + return 8; + if (dtype == torch::kInt16) + return 16; + if (dtype == torch::kInt32) + return 32; + if (dtype == torch::kFloat32) + return 32; + throw std::runtime_error("Unsupported dtype."); + } + throw std::runtime_error("Unsupported file type."); +} + +sox_signalinfo_t get_signalinfo( + const torch::Tensor& tensor, + const int64_t sample_rate, + const bool channels_first, + const std::string filetype) { + return sox_signalinfo_t{ + /*rate=*/static_cast(sample_rate), + /*channels=*/static_cast(tensor.size(channels_first ? 0 : 1)), + /*precision=*/get_precision(filetype, tensor.dtype()), + /*length=*/static_cast(tensor.numel())}; +} + +sox_encodinginfo_t get_encodinginfo( + const std::string filetype, + const caffe2::TypeMeta dtype, + const double compression) { + const double compression_ = [&]() { + if (filetype == "mp3") + return compression; + if (filetype == "flac") + return compression; + if (filetype == "ogg" || filetype == "vorbis") + return compression; + if (filetype == "wav") + return 0.; + throw std::runtime_error("Unsupported file type."); + }(); + + return sox_encodinginfo_t{/*encoding=*/get_encoding(filetype, dtype), + /*bits_per_sample=*/get_precision(filetype, dtype), + /*compression=*/compression_, + /*reverse_bytes=*/sox_option_default, + /*reverse_nibbles=*/sox_option_default, + /*reverse_bits=*/sox_option_default, + /*opposite_endian=*/sox_false}; +} + +} // namespace sox_utils +} // namespace torchaudio diff --git a/torchaudio/csrc/sox_utils.h b/torchaudio/csrc/sox_utils.h new file mode 100644 index 0000000000..665187c840 --- /dev/null +++ b/torchaudio/csrc/sox_utils.h @@ -0,0 +1,100 @@ +#ifndef TORCHAUDIO_SOX_UTILS_H +#define TORCHAUDIO_SOX_UTILS_H + +#include +#include + +namespace torchaudio { +namespace sox_utils { + +struct TensorSignal : torch::CustomClassHolder { + torch::Tensor tensor; + int64_t sample_rate; + bool channels_first; + + TensorSignal( + torch::Tensor tensor_, + int64_t sample_rate_, + bool channels_first_); + + torch::Tensor getTensor() const; + int64_t getSampleRate() const; + bool getChannelsFirst() const; +}; + +/// helper class to automatically close sox_format_t* +struct SoxFormat { + explicit SoxFormat(sox_format_t* fd) noexcept; + SoxFormat(const SoxFormat& other) = delete; + SoxFormat(SoxFormat&& other) = delete; + SoxFormat& operator=(const SoxFormat& other) = delete; + SoxFormat& operator=(SoxFormat&& other) = delete; + ~SoxFormat(); + sox_format_t* operator->() const noexcept; + operator sox_format_t*() const noexcept; + + private: + sox_format_t* fd_; +}; + +/// +/// Verify that input file is found, has known encoding, and not empty +void validate_input_file(const SoxFormat& sf); + +/// +/// Verify that input Tensor is 2D, CPU and either uin8, int16, int32 or float32 +void validate_input_tensor(const torch::Tensor); + +/// +/// Get target dtype for the given encoding and precision. +caffe2::TypeMeta get_dtype( + const sox_encoding_t encoding, + const unsigned precision); + +/// +/// Convert sox_sample_t buffer to uint8/int16/int32/float32 Tensor +/// NOTE: This function might modify the values in the input buffer to +/// reduce the number of memory copy. +/// @param buffer Pointer to buffer that contains audio data. +/// @param num_samples The number of samples to read. +/// @param num_channels The number of channels. Used to reshape the resulting +/// Tensor. +/// @param dtype Target dtype. Determines the output dtype and value range in +/// conjunction with normalization. +/// @param noramlize Perform normalization. Only effective when dtype is not +/// kFloat32. When effective, the output tensor is kFloat32 type and value range +/// is [-1.0, 1.0] +/// @param channels_first When True, output Tensor has shape of [num_channels, +/// num_frames]. +torch::Tensor convert_to_tensor( + sox_sample_t* buffer, + const int32_t num_samples, + const int32_t num_channels, + const caffe2::TypeMeta dtype, + const bool normalize, + const bool channels_first); + +/// +/// Convert float32/int32/int16/uint8 Tensor to int32 for Torch -> Sox +/// conversion. +torch::Tensor unnormalize_wav(const torch::Tensor); + +/// Extract extension from file path +const std::string get_filetype(const std::string path); + +/// Get sox_signalinfo_t for passing a torch::Tensor object. +sox_signalinfo_t get_signalinfo( + const torch::Tensor& tensor, + const int64_t sample_rate, + const bool channels_first, + const std::string filetype); + +/// Get sox_encofinginfo_t for saving audoi file +sox_encodinginfo_t get_encodinginfo( + const std::string filetype, + const caffe2::TypeMeta dtype, + const double compression); + +} // namespace sox_utils +} // namespace torchaudio +#endif diff --git a/torchaudio/csrc/typedefs.cpp b/torchaudio/csrc/typedefs.cpp deleted file mode 100644 index 7b81d665dc..0000000000 --- a/torchaudio/csrc/typedefs.cpp +++ /dev/null @@ -1,23 +0,0 @@ -#include - -namespace torchaudio { -SignalInfo::SignalInfo( - const int64_t sample_rate_, - const int64_t num_channels_, - const int64_t num_samples_) - : sample_rate(sample_rate_), - num_channels(num_channels_), - num_samples(num_samples_){}; - -int64_t SignalInfo::getSampleRate() const { - return sample_rate; -} - -int64_t SignalInfo::getNumChannels() const { - return num_channels; -} - -int64_t SignalInfo::getNumSamples() const { - return num_samples; -} -} // namespace torchaudio diff --git a/torchaudio/csrc/typedefs.h b/torchaudio/csrc/typedefs.h deleted file mode 100644 index 646ed09f3d..0000000000 --- a/torchaudio/csrc/typedefs.h +++ /dev/null @@ -1,23 +0,0 @@ -#ifndef TORCHAUDIO_TYPDEFS_H -#define TORCHAUDIO_TYPDEFS_H - -#include - -namespace torchaudio { -struct SignalInfo : torch::CustomClassHolder { - int64_t sample_rate; - int64_t num_channels; - int64_t num_samples; - - SignalInfo( - const int64_t sample_rate_, - const int64_t num_channels_, - const int64_t num_samples_); - int64_t getSampleRate() const; - int64_t getNumChannels() const; - int64_t getNumSamples() const; -}; - -} // namespace torchaudio - -#endif diff --git a/torchaudio/extension/extension.py b/torchaudio/extension/extension.py index 4a2ab82124..b01ba13e39 100644 --- a/torchaudio/extension/extension.py +++ b/torchaudio/extension/extension.py @@ -12,38 +12,9 @@ def _init_extension(): _init_script_module(ext) else: warnings.warn('torchaudio C++ extension is not available.') - _init_dummy_module() def _init_script_module(module): path = importlib.util.find_spec(module).origin torch.classes.load_library(path) torch.ops.load_library(path) - - -def _init_dummy_module(): - class SignalInfo: - """Data class for audio format information - - Used when torchaudio C++ extension is not available for annotating - sox_io backend functions so that torchaudio is still importable - without extension. - This class has to implement the same interface as C++ equivalent. - """ - def __init__(self, sample_rate: int, num_channels: int, num_samples: int): - self.sample_rate = sample_rate - self.num_channels = num_channels - self.num_samples = num_samples - - def get_sample_rate(self): - return self.sample_rate - - def get_num_channels(self): - return self.num_channels - - def get_num_samples(self): - return self.num_samples - - DummyModule = namedtuple('torchaudio', ['SignalInfo']) - module = DummyModule(SignalInfo) - setattr(torch.classes, 'torchaudio', module) diff --git a/torchaudio/functional.py b/torchaudio/functional.py index 28cb6a3fa2..78c8c594c9 100644 --- a/torchaudio/functional.py +++ b/torchaudio/functional.py @@ -485,9 +485,10 @@ def complex_norm( Returns: Tensor: Power of the normed input tensor. Shape of `(..., )` """ - if power == 1.0: - return torch.norm(complex_tensor, 2, -1) - return torch.norm(complex_tensor, 2, -1).pow(power) + + # Replace by torch.norm once issue is fixed + # https://github.com/pytorch/pytorch/issues/34279 + return complex_tensor.pow(2.).sum(-1).pow(0.5 * power) def angle( diff --git a/torchaudio/models/_wavernn.py b/torchaudio/models/_wavernn.py index 04155fb87c..cd2e89a10c 100644 --- a/torchaudio/models/_wavernn.py +++ b/torchaudio/models/_wavernn.py @@ -1,105 +1,331 @@ +from typing import List + +import torch from torch import Tensor from torch import nn -__all__ = ["_ResBlock", "_MelResNet"] +__all__ = ["_ResBlock", "_MelResNet", "_Stretch2d", "_UpsampleNetwork", "_WaveRNN"] class _ResBlock(nn.Module): - r"""This is a ResNet block layer. This layer is based on the paper "Deep Residual Learning - for Image Recognition". Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. CVPR, 2016. - It is a block used in WaveRNN. WaveRNN is based on the paper "Efficient Neural Audio Synthesis". - Nal Kalchbrenner, Erich Elsen, Karen Simonyan, Seb Noury, Norman Casagrande, Edward Lockhart, - Florian Stimberg, Aaron van den Oord, Sander Dieleman, Koray Kavukcuoglu. arXiv:1802.08435, 2018. + r"""ResNet block based on "Deep Residual Learning for Image Recognition" + + The paper link is https://arxiv.org/pdf/1512.03385.pdf. Args: - num_dims: the number of compute dimensions in the input (default=128). + n_freq: the number of bins in a spectrogram (default=128) - Examples:: - >>> resblock = _ResBlock(num_dims=128) - >>> input = torch.rand(10, 128, 512) - >>> output = resblock(input) + Examples + >>> resblock = _ResBlock() + >>> input = torch.rand(10, 128, 512) # a random spectrogram + >>> output = resblock(input) # shape: (10, 128, 512) """ - def __init__(self, num_dims: int = 128) -> None: + def __init__(self, n_freq: int = 128) -> None: super().__init__() self.resblock_model = nn.Sequential( - nn.Conv1d(in_channels=num_dims, out_channels=num_dims, kernel_size=1, bias=False), - nn.BatchNorm1d(num_dims), + nn.Conv1d(in_channels=n_freq, out_channels=n_freq, kernel_size=1, bias=False), + nn.BatchNorm1d(n_freq), nn.ReLU(inplace=True), - nn.Conv1d(in_channels=num_dims, out_channels=num_dims, kernel_size=1, bias=False), - nn.BatchNorm1d(num_dims) + nn.Conv1d(in_channels=n_freq, out_channels=n_freq, kernel_size=1, bias=False), + nn.BatchNorm1d(n_freq) ) - def forward(self, x: Tensor) -> Tensor: + def forward(self, specgram: Tensor) -> Tensor: r"""Pass the input through the _ResBlock layer. - Args: - x: the input sequence to the _ResBlock layer (required). + specgram (Tensor): the input sequence to the _ResBlock layer (n_batch, n_freq, n_time). - Shape: - - x: :math:`(N, S, T)`. - - output: :math:`(N, S, T)`. - where N is the batch size, S is the number of input sequence, - T is the length of input sequence. + Return: + Tensor shape: (n_batch, n_freq, n_time) """ - residual = x - return self.resblock_model(x) + residual + return self.resblock_model(specgram) + specgram class _MelResNet(nn.Module): - r"""This is a MelResNet layer based on a stack of ResBlocks. It is a block used in WaveRNN. - WaveRNN is based on the paper "Efficient Neural Audio Synthesis". Nal Kalchbrenner, Erich Elsen, - Karen Simonyan, Seb Noury, Norman Casagrande, Edward Lockhart, Florian Stimberg, Aaron van den Oord, - Sander Dieleman, Koray Kavukcuoglu. arXiv:1802.08435, 2018. + r"""MelResNet layer uses a stack of ResBlocks on spectrogram. Args: - res_blocks: the number of ResBlock in stack (default=10). - input_dims: the number of input sequence (default=100). - hidden_dims: the number of compute dimensions (default=128). - output_dims: the number of output sequence (default=128). - pad: the number of kernal size (pad * 2 + 1) in the first Conv1d layer (default=2). - - Examples:: - >>> melresnet = _MelResNet(res_blocks=10, input_dims=100, - hidden_dims=128, output_dims=128, pad=2) - >>> input = torch.rand(10, 100, 512) - >>> output = melresnet(input) + n_res_block: the number of ResBlock in stack (default=10) + n_freq: the number of bins in a spectrogram (default=128) + n_hidden: the number of hidden dimensions (default=128) + n_output: the number of output dimensions (default=128) + kernel_size: the number of kernel size in the first Conv1d layer (default=5) + + Examples + >>> melresnet = _MelResNet() + >>> input = torch.rand(10, 128, 512) # a random spectrogram + >>> output = melresnet(input) # shape: (10, 128, 508) """ - def __init__(self, res_blocks: int = 10, - input_dims: int = 100, - hidden_dims: int = 128, - output_dims: int = 128, - pad: int = 2) -> None: + def __init__(self, + n_res_block: int = 10, + n_freq: int = 128, + n_hidden: int = 128, + n_output: int = 128, + kernel_size: int = 5) -> None: super().__init__() - kernel_size = pad * 2 + 1 - ResBlocks = [] - - for i in range(res_blocks): - ResBlocks.append(_ResBlock(hidden_dims)) + ResBlocks = [_ResBlock(n_hidden) for _ in range(n_res_block)] self.melresnet_model = nn.Sequential( - nn.Conv1d(in_channels=input_dims, out_channels=hidden_dims, kernel_size=kernel_size, bias=False), - nn.BatchNorm1d(hidden_dims), + nn.Conv1d(in_channels=n_freq, out_channels=n_hidden, kernel_size=kernel_size, bias=False), + nn.BatchNorm1d(n_hidden), nn.ReLU(inplace=True), *ResBlocks, - nn.Conv1d(in_channels=hidden_dims, out_channels=output_dims, kernel_size=1) + nn.Conv1d(in_channels=n_hidden, out_channels=n_output, kernel_size=1) ) - def forward(self, x: Tensor) -> Tensor: + def forward(self, specgram: Tensor) -> Tensor: r"""Pass the input through the _MelResNet layer. + Args: + specgram (Tensor): the input sequence to the _MelResNet layer (n_batch, n_freq, n_time). + + Return: + Tensor shape: (n_batch, n_output, n_time - kernel_size + 1) + """ + + return self.melresnet_model(specgram) + + +class _Stretch2d(nn.Module): + r"""Upscale the frequency and time dimensions of a spectrogram. + + Args: + time_scale: the scale factor in time dimension + freq_scale: the scale factor in frequency dimension + + Examples + >>> stretch2d = _Stretch2d(time_scale=10, freq_scale=5) + + >>> input = torch.rand(10, 100, 512) # a random spectrogram + >>> output = stretch2d(input) # shape: (10, 500, 5120) + """ + + def __init__(self, + time_scale: int, + freq_scale: int) -> None: + super().__init__() + + self.freq_scale = freq_scale + self.time_scale = time_scale + + def forward(self, specgram: Tensor) -> Tensor: + r"""Pass the input through the _Stretch2d layer. Args: - x: the input sequence to the _MelResNet layer (required). + specgram (Tensor): the input sequence to the _Stretch2d layer (..., n_freq, n_time). - Shape: - - x: :math:`(N, S, T)`. - - output: :math:`(N, P, T - 2 * pad)`. - where N is the batch size, S is the number of input sequence, - P is the number of output sequence, T is the length of input sequence. + Return: + Tensor shape: (..., n_freq * freq_scale, n_time * time_scale) """ - return self.melresnet_model(x) + return specgram.repeat_interleave(self.freq_scale, -2).repeat_interleave(self.time_scale, -1) + + +class _UpsampleNetwork(nn.Module): + r"""Upscale the dimensions of a spectrogram. + + Args: + upsample_scales: the list of upsample scales + n_res_block: the number of ResBlock in stack (default=10) + n_freq: the number of bins in a spectrogram (default=128) + n_hidden: the number of hidden dimensions (default=128) + n_output: the number of output dimensions (default=128) + kernel_size: the number of kernel size in the first Conv1d layer (default=5) + + Examples + >>> upsamplenetwork = _UpsampleNetwork(upsample_scales=[4, 4, 16]) + >>> input = torch.rand(10, 128, 10) # a random spectrogram + >>> output = upsamplenetwork(input) # shape: (10, 1536, 128), (10, 1536, 128) + """ + + def __init__(self, + upsample_scales: List[int], + n_res_block: int = 10, + n_freq: int = 128, + n_hidden: int = 128, + n_output: int = 128, + kernel_size: int = 5) -> None: + super().__init__() + + total_scale = 1 + for upsample_scale in upsample_scales: + total_scale *= upsample_scale + + self.indent = (kernel_size - 1) // 2 * total_scale + self.resnet = _MelResNet(n_res_block, n_freq, n_hidden, n_output, kernel_size) + self.resnet_stretch = _Stretch2d(total_scale, 1) + + up_layers = [] + for scale in upsample_scales: + stretch = _Stretch2d(scale, 1) + conv = nn.Conv2d(in_channels=1, + out_channels=1, + kernel_size=(1, scale * 2 + 1), + padding=(0, scale), + bias=False) + conv.weight.data.fill_(1. / (scale * 2 + 1)) + up_layers.append(stretch) + up_layers.append(conv) + self.upsample_layers = nn.Sequential(*up_layers) + + def forward(self, specgram: Tensor) -> Tensor: + r"""Pass the input through the _UpsampleNetwork layer. + + Args: + specgram (Tensor): the input sequence to the _UpsampleNetwork layer (n_batch, n_freq, n_time) + + Return: + Tensor shape: (n_batch, n_freq, (n_time - kernel_size + 1) * total_scale), + (n_batch, n_output, (n_time - kernel_size + 1) * total_scale) + where total_scale is the product of all elements in upsample_scales. + """ + + resnet_output = self.resnet(specgram).unsqueeze(1) + resnet_output = self.resnet_stretch(resnet_output) + resnet_output = resnet_output.squeeze(1) + + specgram = specgram.unsqueeze(1) + upsampling_output = self.upsample_layers(specgram) + upsampling_output = upsampling_output.squeeze(1)[:, :, self.indent:-self.indent] + + return upsampling_output, resnet_output + + +class _WaveRNN(nn.Module): + r"""WaveRNN model based on the implementation from `fatchord `_. + + The original implementation was introduced in + `"Efficient Neural Audio Synthesis" `_. + The input channels of waveform and spectrogram have to be 1. The product of + `upsample_scales` must equal `hop_length`. + + Args: + upsample_scales: the list of upsample scales + n_bits: the bits of output waveform + sample_rate: the rate of audio dimensions (samples per second) + hop_length: the number of samples between the starts of consecutive frames + n_res_block: the number of ResBlock in stack (default=10) + n_rnn: the dimension of RNN layer (default=512) + n_fc: the dimension of fully connected layer (default=512) + kernel_size: the number of kernel size in the first Conv1d layer (default=5) + n_freq: the number of bins in a spectrogram (default=128) + n_hidden: the number of hidden dimensions (default=128) + n_output: the number of output dimensions (default=128) + mode: the mode of waveform in ['waveform', 'mol'] (default='waveform') + + Example + >>> wavernn = _waveRNN(upsample_scales=[5,5,8], n_bits=9, sample_rate=24000, hop_length=200) + >>> waveform, sample_rate = torchaudio.load(file) + >>> # waveform shape: (n_batch, n_channel, (n_time - kernel_size + 1) * hop_length) + >>> specgram = MelSpectrogram(sample_rate)(waveform) # shape: (n_batch, n_channel, n_freq, n_time) + >>> output = wavernn(waveform, specgram) + >>> # output shape: (n_batch, n_channel, (n_time - kernel_size + 1) * hop_length, 2 ** n_bits) + """ + + def __init__(self, + upsample_scales: List[int], + n_bits: int, + sample_rate: int, + hop_length: int, + n_res_block: int = 10, + n_rnn: int = 512, + n_fc: int = 512, + kernel_size: int = 5, + n_freq: int = 128, + n_hidden: int = 128, + n_output: int = 128, + mode: str = 'waveform') -> None: + super().__init__() + + self.mode = mode + self.kernel_size = kernel_size + + if self.mode == 'waveform': + self.n_classes = 2 ** n_bits + elif self.mode == 'mol': + self.n_classes = 30 + else: + raise ValueError(f"Expected mode: `waveform` or `mol`, but found {self.mode}") + + self.n_rnn = n_rnn + self.n_aux = n_output // 4 + self.hop_length = hop_length + self.sample_rate = sample_rate + + total_scale = 1 + for upsample_scale in upsample_scales: + total_scale *= upsample_scale + if total_scale != self.hop_length: + raise ValueError(f"Expected: total_scale == hop_length, but found {total_scale} != {hop_length}") + + self.upsample = _UpsampleNetwork(upsample_scales, n_res_block, n_freq, n_hidden, n_output, kernel_size) + self.fc = nn.Linear(n_freq + self.n_aux + 1, n_rnn) + + self.rnn1 = nn.GRU(n_rnn, n_rnn, batch_first=True) + self.rnn2 = nn.GRU(n_rnn + self.n_aux, n_rnn, batch_first=True) + + self.relu1 = nn.ReLU(inplace=True) + self.relu2 = nn.ReLU(inplace=True) + + self.fc1 = nn.Linear(n_rnn + self.n_aux, n_fc) + self.fc2 = nn.Linear(n_fc + self.n_aux, n_fc) + self.fc3 = nn.Linear(n_fc, self.n_classes) + + def forward(self, waveform: Tensor, specgram: Tensor) -> Tensor: + r"""Pass the input through the _WaveRNN model. + + Args: + waveform: the input waveform to the _WaveRNN layer (n_batch, 1, (n_time - kernel_size + 1) * hop_length) + specgram: the input spectrogram to the _WaveRNN layer (n_batch, 1, n_freq, n_time) + + Return: + Tensor shape: (n_batch, 1, (n_time - kernel_size + 1) * hop_length, 2 ** n_bits) + """ + + assert waveform.size(1) == 1, 'Require the input channel of waveform is 1' + assert specgram.size(1) == 1, 'Require the input channel of specgram is 1' + # remove channel dimension until the end + waveform, specgram = waveform.squeeze(1), specgram.squeeze(1) + + batch_size = waveform.size(0) + h1 = torch.zeros(1, batch_size, self.n_rnn, dtype=waveform.dtype, device=waveform.device) + h2 = torch.zeros(1, batch_size, self.n_rnn, dtype=waveform.dtype, device=waveform.device) + # output of upsample: + # specgram: (n_batch, n_freq, (n_time - kernel_size + 1) * total_scale) + # aux: (n_batch, n_output, (n_time - kernel_size + 1) * total_scale) + specgram, aux = self.upsample(specgram) + specgram = specgram.transpose(1, 2) + aux = aux.transpose(1, 2) + + aux_idx = [self.n_aux * i for i in range(5)] + a1 = aux[:, :, aux_idx[0]:aux_idx[1]] + a2 = aux[:, :, aux_idx[1]:aux_idx[2]] + a3 = aux[:, :, aux_idx[2]:aux_idx[3]] + a4 = aux[:, :, aux_idx[3]:aux_idx[4]] + + x = torch.cat([waveform.unsqueeze(-1), specgram, a1], dim=-1) + x = self.fc(x) + res = x + x, _ = self.rnn1(x, h1) + + x = x + res + res = x + x = torch.cat([x, a2], dim=-1) + x, _ = self.rnn2(x, h2) + + x = x + res + x = torch.cat([x, a3], dim=-1) + x = self.fc1(x) + x = self.relu1(x) + + x = torch.cat([x, a4], dim=-1) + x = self.fc2(x) + x = self.relu2(x) + x = self.fc3(x) + + # bring back channel dimension + return x.unsqueeze(1) diff --git a/torchaudio/sox_effects/__init__.py b/torchaudio/sox_effects/__init__.py index 115b70c895..507dc5c3af 100644 --- a/torchaudio/sox_effects/__init__.py +++ b/torchaudio/sox_effects/__init__.py @@ -9,4 +9,6 @@ if _mod_utils.is_module_available('torchaudio._torchaudio'): + import atexit init_sox_effects() + atexit.register(shutdown_sox_effects) diff --git a/torchaudio/sox_effects/sox_effects.py b/torchaudio/sox_effects/sox_effects.py index 6a6b5be0a5..0aee312126 100644 --- a/torchaudio/sox_effects/sox_effects.py +++ b/torchaudio/sox_effects/sox_effects.py @@ -1,4 +1,3 @@ -import atexit from typing import Any, Callable, List, Optional, Tuple, Union import torch @@ -13,19 +12,8 @@ from torchaudio import _torchaudio -_SOX_INITIALIZED: Optional[bool] = False -# This variable has a micro lifecycle. (False -> True -> None) -# False: Not initialized -# True: Initialized -# None: Already shut down (should not be initialized again.) - -_SOX_SUCCESS_CODE = 0 -# defined at -# https://fossies.org/dox/sox-14.4.2/sox_8h.html#a8e07e80cebeff3339265d89c387cea93a9ef2b87ec303edfe40751d9a85fadeeb - - @_mod_utils.requires_module('torchaudio._torchaudio') -def init_sox_effects() -> int: +def init_sox_effects() -> None: """Initialize resources required to use ``SoxEffectsChain`` You do not need to call this function manually. It is called automatically. @@ -33,50 +21,26 @@ def init_sox_effects() -> int: Once initialized, you do not need to call this function again across the multiple call of ``SoxEffectsChain.sox_build_flow_effects``, though it is safe to do so as long as ``shutdown_sox_effects`` is not called yet. - Once ``shutdown_sox_effects`` is called, you can no longer use SoX effects and calling - this function results in `RuntimeError`. + Once ``shutdown_sox_effects`` is called, you can no longer use SoX effects and + initializing again will result in error. Note: This function is not required for simple loading. - - Returns: - int: Code corresponding to sox_error_t enum. See - https://fossies.org/dox/sox-14.4.2/sox_8h.html#a8e07e80cebeff3339265d89c387cea93 """ - global _SOX_INITIALIZED - if _SOX_INITIALIZED is None: - raise RuntimeError('SoX effects chain has been already shut down. Can not initialize again.') - if not _SOX_INITIALIZED: - code = _torchaudio.initialize_sox() - if code == _SOX_SUCCESS_CODE: - _SOX_INITIALIZED = True - atexit.register(shutdown_sox_effects) - return code - return _SOX_SUCCESS_CODE + torch.ops.torchaudio.sox_effects_initialize_sox_effects() @_mod_utils.requires_module("torchaudio._torchaudio") -def shutdown_sox_effects() -> int: +def shutdown_sox_effects() -> None: """Clean up resources required to use ``SoxEffectsChain`` You do not need to call this function manually. It is called automatically. It is safe to call this function multiple times. - Once ``shutdown_sox_effects`` is called, you can no longer use SoX effects and calling - this function results in `RuntimeError`. - - - Returns: - int: Code corresponding to sox_error_t enum. See - https://fossies.org/dox/sox-14.4.2/sox_8h.html#a8e07e80cebeff3339265d89c387cea93 + Once ``shutdown_sox_effects`` is called, you can no longer use SoX effects and + initializing again will result in error. """ - global _SOX_INITIALIZED - if _SOX_INITIALIZED: - code = _torchaudio.shutdown_sox() - if code == _SOX_INITIALIZED: - _SOX_INITIALIZED = None - return code - return _SOX_SUCCESS_CODE + torch.ops.torchaudio.sox_effects_shutdown_sox_effects() @_mod_utils.requires_module('torchaudio._torchaudio') @@ -88,7 +52,7 @@ def effect_names() -> List[str]: Example >>> EFFECT_NAMES = torchaudio.sox_effects.effect_names() """ - return _torchaudio.get_effect_names() + return torch.ops.torchaudio.sox_effects_list_effects() @_mod_utils.requires_module('torchaudio._torchaudio') From fd7460ecfbfa47ff1612fba939b7dc47aae613dd Mon Sep 17 00:00:00 2001 From: Stanislau Hlebik Date: Fri, 17 Jul 2020 17:07:23 -0700 Subject: [PATCH 11/19] remediation of S205607 fbshipit-source-id: 5113fe0c527595e4227ff827253b7414abbdf7ac --- .python3 | 1 + build_tools/__init__.py | 1 + examples/test/__init__.py | 1 + test/compliance/__init__.py | 1 + test/sox_io_backend/__init__.py | 1 + torchaudio/_internal/__init__.py | 1 + 6 files changed, 6 insertions(+) diff --git a/.python3 b/.python3 index e69de29bb2..56de9c5ee1 100644 --- a/.python3 +++ b/.python3 @@ -0,0 +1 @@ +S205607 diff --git a/build_tools/__init__.py b/build_tools/__init__.py index e69de29bb2..56de9c5ee1 100644 --- a/build_tools/__init__.py +++ b/build_tools/__init__.py @@ -0,0 +1 @@ +S205607 diff --git a/examples/test/__init__.py b/examples/test/__init__.py index e69de29bb2..56de9c5ee1 100644 --- a/examples/test/__init__.py +++ b/examples/test/__init__.py @@ -0,0 +1 @@ +S205607 diff --git a/test/compliance/__init__.py b/test/compliance/__init__.py index e69de29bb2..56de9c5ee1 100644 --- a/test/compliance/__init__.py +++ b/test/compliance/__init__.py @@ -0,0 +1 @@ +S205607 diff --git a/test/sox_io_backend/__init__.py b/test/sox_io_backend/__init__.py index e69de29bb2..56de9c5ee1 100644 --- a/test/sox_io_backend/__init__.py +++ b/test/sox_io_backend/__init__.py @@ -0,0 +1 @@ +S205607 diff --git a/torchaudio/_internal/__init__.py b/torchaudio/_internal/__init__.py index e69de29bb2..56de9c5ee1 100644 --- a/torchaudio/_internal/__init__.py +++ b/torchaudio/_internal/__init__.py @@ -0,0 +1 @@ +S205607 From 4f19eef10cfcef472ab534b5907b3e2e38f2843d Mon Sep 17 00:00:00 2001 From: Stanislau Hlebik Date: Fri, 17 Jul 2020 17:07:23 -0700 Subject: [PATCH 12/19] remediation of S205607 fbshipit-source-id: 798decc90db4f13770e97cdce3c0df7d5421b2a3 --- .python3 | 1 - build_tools/__init__.py | 1 - examples/test/__init__.py | 1 - test/compliance/__init__.py | 1 - test/sox_io_backend/__init__.py | 1 - torchaudio/_internal/__init__.py | 1 - 6 files changed, 6 deletions(-) diff --git a/.python3 b/.python3 index 56de9c5ee1..e69de29bb2 100644 --- a/.python3 +++ b/.python3 @@ -1 +0,0 @@ -S205607 diff --git a/build_tools/__init__.py b/build_tools/__init__.py index 56de9c5ee1..e69de29bb2 100644 --- a/build_tools/__init__.py +++ b/build_tools/__init__.py @@ -1 +0,0 @@ -S205607 diff --git a/examples/test/__init__.py b/examples/test/__init__.py index 56de9c5ee1..e69de29bb2 100644 --- a/examples/test/__init__.py +++ b/examples/test/__init__.py @@ -1 +0,0 @@ -S205607 diff --git a/test/compliance/__init__.py b/test/compliance/__init__.py index 56de9c5ee1..e69de29bb2 100644 --- a/test/compliance/__init__.py +++ b/test/compliance/__init__.py @@ -1 +0,0 @@ -S205607 diff --git a/test/sox_io_backend/__init__.py b/test/sox_io_backend/__init__.py index 56de9c5ee1..e69de29bb2 100644 --- a/test/sox_io_backend/__init__.py +++ b/test/sox_io_backend/__init__.py @@ -1 +0,0 @@ -S205607 diff --git a/torchaudio/_internal/__init__.py b/torchaudio/_internal/__init__.py index 56de9c5ee1..e69de29bb2 100644 --- a/torchaudio/_internal/__init__.py +++ b/torchaudio/_internal/__init__.py @@ -1 +0,0 @@ -S205607 From 57a88cd7d91f0cdeab39b7e1fae55e6bb22bc41c Mon Sep 17 00:00:00 2001 From: Moto Hira Date: Mon, 27 Jul 2020 06:03:09 -0700 Subject: [PATCH 13/19] Import torchaudio 20200723 Summary: Import torchaudio 20200723 #814 Reviewed By: fmassa Differential Revision: D22666393 fbshipit-source-id: 50df07b5c158fe4e95ada7ea54381b2e26f6aecd --- .circleci/config.yml | 1342 +++++++++++++++++ .circleci/config.yml.in | 631 ++++++++ .circleci/regenerate.py | 180 +++ .circleci/smoke_test/docker/Dockerfile | 36 + .circleci/test/test_sort_yaml.py | 14 + .circleci/unittest/linux/README.md | 6 + .circleci/unittest/linux/docker/.dockerignore | 2 + .circleci/unittest/linux/docker/.gitignore | 2 + .circleci/unittest/linux/docker/Dockerfile | 59 + .../unittest/linux/docker/build_and_push.sh | 27 + .../docker/scripts/copy_kaldi_executables.sh | 58 + .../unittest/linux/scripts/environment.yml | 17 + .circleci/unittest/linux/scripts/install.sh | 23 + .../unittest/linux/scripts/post_process.sh | 8 + .../linux/scripts/run_style_checks.sh | 32 + .circleci/unittest/linux/scripts/run_test.sh | 10 + .circleci/unittest/linux/scripts/setup_env.sh | 50 + .circleci/unittest/windows/README.md | 4 + .../unittest/windows/scripts/environment.yml | 18 + .circleci/unittest/windows/scripts/install.sh | 23 + .../windows/scripts/install_conda.bat | 1 + .../unittest/windows/scripts/post_process.sh | 8 + .../unittest/windows/scripts/run_test.sh | 10 + .../unittest/windows/scripts/setup_env.sh | 39 + .clang-format | 88 ++ .clang-tidy | 33 + .flake8 | 4 + .github/ISSUE_TEMPLATE/bug-report.md | 53 + .github/ISSUE_TEMPLATE/documentation.md | 9 + .github/ISSUE_TEMPLATE/feature-request.md | 24 + .../ISSUE_TEMPLATE/questions-help-support.md | 13 + .github/pytorch-probot.yml | 1 + .gitignore | 129 ++ .travis.yml | 32 + docs/source/backend.rst | 138 ++ docs/source/datasets.rst | 8 + docs/source/index.rst | 15 +- docs/source/sox_effects.rst | 47 +- docs/source/torchaudio.rst | 43 + docs/source/utils.rst | 11 + examples/pipeline_wavernn/README.md | 36 + examples/pipeline_wavernn/datasets.py | 115 ++ examples/pipeline_wavernn/losses.py | 119 ++ examples/pipeline_wavernn/main.py | 390 +++++ examples/pipeline_wavernn/processing.py | 58 + examples/pipeline_wavernn/utils.py | 61 + test/README.md | 2 +- test/assets/100Hz_44100Hz_16bit_05sec.wav | Bin 444524 -> 0 bytes test/assets/440Hz_44100Hz_16bit_05sec.wav | Bin 444524 -> 0 bytes test/assets/genres/noise/noise.0000.wav | Bin 223532 -> 0 bytes test/assets/sox_effect_test_args.json | 88 ++ test/assets/sox_effect_test_fir_coeffs.txt | 1 + test/assets/waves_yesno/0_1_0_1_0_1_1_0.wav | Bin 84 -> 0 bytes test/assets/whitenoise.mp3 | Bin 57136 -> 0 bytes test/assets/whitenoise.wav | Bin 441078 -> 0 bytes test/assets/whitenoise_1min.mp3 | Bin 666705 -> 0 bytes test/common_utils/backend_utils.py | 4 +- test/common_utils/case_utils.py | 21 +- test/common_utils/data_utils.py | 44 +- test/common_utils/sox_utils.py | 21 + test/datasets/__init__.py | 0 .../datasets_test.py} | 29 +- test/datasets/gtzan_test.py | 88 ++ test/datasets/libritts_test.py | 71 + test/datasets/utils_test.py | 47 + test/datasets/yesno_test.py | 49 + test/functional_cpu_test.py | 31 +- test/sox_effect/__init__.py | 0 test/sox_effect/common.py | 26 + test/sox_effect/smoke_test.py | 59 + .../sox_effects_chain_test.py} | 58 +- test/sox_effect/test_dataset.py | 114 ++ test/sox_effect/test_sox_effect.py | 221 +++ test/sox_effect/test_torchscript.py | 100 ++ test/sox_io_backend/smoke_test.py | 87 ++ test/test_batch_consistency.py | 32 +- test/test_compliance_kaldi.py | 59 +- test/test_dataloader.py | 4 - test/test_io.py | 3 - test/test_kaldi_io.py | 4 - test/test_librosa_compatibility.py | 11 +- test/test_models.py | 47 +- test/test_sox_compatibility.py | 534 +++---- test/test_transforms.py | 4 - test/utils/__init__.py | 0 test/utils/test_sox_utils.py | 49 + torchaudio/__init__.py | 7 +- torchaudio/_internal/module_utils.py | 2 +- torchaudio/backend/common.py | 26 + torchaudio/backend/sox_io_backend.py | 159 +- torchaudio/backend/utils.py | 19 +- torchaudio/csrc/register.cpp | 33 +- torchaudio/csrc/sox_effects.cpp | 135 +- torchaudio/csrc/sox_effects.h | 11 +- torchaudio/csrc/sox_effects_chain.cpp | 287 ++++ torchaudio/csrc/sox_effects_chain.h | 42 + torchaudio/csrc/sox_io.cpp | 98 +- torchaudio/csrc/sox_utils.cpp | 66 +- torchaudio/csrc/sox_utils.h | 32 +- torchaudio/datasets/__init__.py | 2 + torchaudio/datasets/gtzan.py | 49 +- torchaudio/datasets/libritts.py | 131 ++ torchaudio/datasets/utils.py | 8 +- torchaudio/functional.py | 4 +- torchaudio/models/_wavernn.py | 80 +- torchaudio/models/wav2letter.py | 9 +- torchaudio/sox_effects/__init__.py | 2 + torchaudio/sox_effects/sox_effects.py | 322 +++- torchaudio/transforms.py | 37 +- torchaudio/utils/__init__.py | 9 + torchaudio/utils/sox_utils.py | 94 ++ 111 files changed, 6697 insertions(+), 902 deletions(-) create mode 100644 .circleci/config.yml create mode 100644 .circleci/config.yml.in create mode 100755 .circleci/regenerate.py create mode 100644 .circleci/smoke_test/docker/Dockerfile create mode 100755 .circleci/test/test_sort_yaml.py create mode 100644 .circleci/unittest/linux/README.md create mode 100644 .circleci/unittest/linux/docker/.dockerignore create mode 100644 .circleci/unittest/linux/docker/.gitignore create mode 100644 .circleci/unittest/linux/docker/Dockerfile create mode 100755 .circleci/unittest/linux/docker/build_and_push.sh create mode 100755 .circleci/unittest/linux/docker/scripts/copy_kaldi_executables.sh create mode 100644 .circleci/unittest/linux/scripts/environment.yml create mode 100755 .circleci/unittest/linux/scripts/install.sh create mode 100755 .circleci/unittest/linux/scripts/post_process.sh create mode 100755 .circleci/unittest/linux/scripts/run_style_checks.sh create mode 100755 .circleci/unittest/linux/scripts/run_test.sh create mode 100755 .circleci/unittest/linux/scripts/setup_env.sh create mode 100644 .circleci/unittest/windows/README.md create mode 100644 .circleci/unittest/windows/scripts/environment.yml create mode 100644 .circleci/unittest/windows/scripts/install.sh create mode 100644 .circleci/unittest/windows/scripts/install_conda.bat create mode 100644 .circleci/unittest/windows/scripts/post_process.sh create mode 100644 .circleci/unittest/windows/scripts/run_test.sh create mode 100644 .circleci/unittest/windows/scripts/setup_env.sh create mode 100644 .clang-format create mode 100644 .clang-tidy create mode 100644 .flake8 create mode 100644 .github/ISSUE_TEMPLATE/bug-report.md create mode 100644 .github/ISSUE_TEMPLATE/documentation.md create mode 100644 .github/ISSUE_TEMPLATE/feature-request.md create mode 100644 .github/ISSUE_TEMPLATE/questions-help-support.md create mode 100644 .github/pytorch-probot.yml create mode 100644 .gitignore create mode 100644 .travis.yml create mode 100644 docs/source/backend.rst create mode 100644 docs/source/torchaudio.rst create mode 100644 docs/source/utils.rst create mode 100644 examples/pipeline_wavernn/README.md create mode 100644 examples/pipeline_wavernn/datasets.py create mode 100644 examples/pipeline_wavernn/losses.py create mode 100644 examples/pipeline_wavernn/main.py create mode 100644 examples/pipeline_wavernn/processing.py create mode 100644 examples/pipeline_wavernn/utils.py delete mode 100644 test/assets/100Hz_44100Hz_16bit_05sec.wav delete mode 100644 test/assets/440Hz_44100Hz_16bit_05sec.wav delete mode 100644 test/assets/genres/noise/noise.0000.wav create mode 100644 test/assets/sox_effect_test_args.json create mode 100644 test/assets/sox_effect_test_fir_coeffs.txt delete mode 100644 test/assets/waves_yesno/0_1_0_1_0_1_1_0.wav delete mode 100644 test/assets/whitenoise.mp3 delete mode 100644 test/assets/whitenoise.wav delete mode 100644 test/assets/whitenoise_1min.mp3 create mode 100644 test/datasets/__init__.py rename test/{test_datasets.py => datasets/datasets_test.py} (71%) create mode 100644 test/datasets/gtzan_test.py create mode 100644 test/datasets/libritts_test.py create mode 100644 test/datasets/utils_test.py create mode 100644 test/datasets/yesno_test.py create mode 100644 test/sox_effect/__init__.py create mode 100644 test/sox_effect/common.py create mode 100644 test/sox_effect/smoke_test.py rename test/{test_sox_effects.py => sox_effect/sox_effects_chain_test.py} (82%) create mode 100644 test/sox_effect/test_dataset.py create mode 100644 test/sox_effect/test_sox_effect.py create mode 100644 test/sox_effect/test_torchscript.py create mode 100644 test/sox_io_backend/smoke_test.py create mode 100644 test/utils/__init__.py create mode 100644 test/utils/test_sox_utils.py create mode 100644 torchaudio/csrc/sox_effects_chain.cpp create mode 100644 torchaudio/csrc/sox_effects_chain.h create mode 100644 torchaudio/datasets/libritts.py create mode 100644 torchaudio/utils/__init__.py create mode 100644 torchaudio/utils/sox_utils.py diff --git a/.circleci/config.yml b/.circleci/config.yml new file mode 100644 index 0000000000..771dccc515 --- /dev/null +++ b/.circleci/config.yml @@ -0,0 +1,1342 @@ +version: 2.1 + +# How to test the Linux jobs: +# - Install CircleCI local CLI: https://circleci.com/docs/2.0/local-cli/ +# - circleci config process .circleci/config.yml > gen.yml && circleci local execute -c gen.yml --job binary_linux_wheel_py3.8 +# - Replace binary_linux_wheel_py3.8 with the name of the job you want to test. +# Job names are 'name:' key. + +executors: + windows-cpu: + machine: + resource_class: windows.xlarge + image: windows-server-2019-vs2019:stable + shell: bash.exe + + windows-gpu: + machine: + resource_class: windows.gpu.nvidia.medium + image: windows-server-2019-nvidia:stable + shell: bash.exe + +commands: + generate_cache_key: + description: "Generates a cache key file that changes daily" + steps: + - run: + name: Generate cache key + command: echo "$(date +"%Y-%m-%d")" > .cachekey + designate_upload_channel: + description: "inserts the correct upload channel into ${BASH_ENV}" + steps: + - run: + name: adding UPLOAD_CHANNEL to BASH_ENV + command: | + our_upload_channel=nightly + # On tags upload to test instead + if [[ -n "${CIRCLE_TAG}" ]]; then + our_upload_channel=test + fi + echo "export UPLOAD_CHANNEL=${our_upload_channel}" >> ${BASH_ENV} + install_build_tools_macos: + description: "installs tools required to build torchaudio" + steps: + - run: + name: Install cmake and pkg-config + command: HOMEBREW_NO_AUTO_UPDATE=1 brew install cmake pkg-config wget + # Disable brew auto update which is very slow + +binary_common: &binary_common + parameters: + # Edit these defaults to do a release + build_version: + description: "version number of release binary; by default, build a nightly" + type: string + default: "" + pytorch_version: + description: "PyTorch version to build against; by default, use a nightly" + type: string + default: "" + # Don't edit these + python_version: + description: "Python version to build against (e.g., 3.8)" + type: string + environment: + PYTHON_VERSION: << parameters.python_version >> + BUILD_VERSION: << parameters.build_version >> + PYTORCH_VERSION: << parameters.pytorch_version >> + CU_VERSION: cpu + +smoke_test_common: &smoke_test_common + <<: *binary_common + docker: + - image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/torchaudio/smoke_test:56c846a5-acaa-41a7-92f5-46ec66186c61 + aws_auth: + aws_access_key_id: ${ECR_AWS_ACCESS_KEY} + aws_secret_access_key: ${ECR_AWS_SECRET_ACCESS_KEY} + +jobs: + circleci_consistency: + docker: + - image: circleci/python:3.8 + steps: + - checkout + - run: + command: | + pip install --user --progress-bar off jinja2 pyyaml + python .circleci/regenerate.py + git diff --exit-code || (echo ".circleci/config.yml not in sync with config.yml.in! Run .circleci/regenerate.py to update config"; exit 1) + + download_third_parties_nix: + docker: + - image: "pytorch/torchaudio_unittest_base:manylinux" + resource_class: small + steps: + - checkout + - generate_cache_key + - restore_cache: + + keys: + - tp-nix-v2-{{ checksum ".cachekey" }} + + - run: + command: | + mkdir -p third_party/archives/ + wget --no-clobber --directory-prefix=third_party/archives/ $(awk '/URL /{print $2}' third_party/CMakeLists.txt) + - save_cache: + + key: tp-nix-v2-{{ checksum ".cachekey" }} + + paths: + - third_party/archives + - persist_to_workspace: + root: third_party + paths: + - archives + + binary_linux_wheel: + <<: *binary_common + docker: + - image: "pytorch/manylinux-cuda100" + resource_class: 2xlarge+ + steps: + - checkout + - attach_workspace: + at: third_party + - run: packaging/build_wheel.sh + - store_artifacts: + path: dist + - persist_to_workspace: + root: dist + paths: + - "*" + + binary_linux_conda: + <<: *binary_common + docker: + - image: "pytorch/conda-cuda" + resource_class: 2xlarge+ + steps: + - checkout + - attach_workspace: + at: third_party + - run: packaging/build_conda.sh + - store_artifacts: + path: /opt/conda/conda-bld/linux-64 + - persist_to_workspace: + root: /opt/conda/conda-bld/linux-64 + paths: + - "*" + + binary_macos_wheel: + <<: *binary_common + macos: + xcode: "9.0" + steps: + - checkout + - install_build_tools_macos + - attach_workspace: + at: third_party + - run: + # Cannot easily deduplicate this as source'ing activate + # will set environment variables which we need to propagate + # to build_wheel.sh + command: | + curl -o conda.sh https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh + sh conda.sh -b + source $HOME/miniconda3/bin/activate + packaging/build_wheel.sh + - store_artifacts: + path: dist + - persist_to_workspace: + root: dist + paths: + - "*" + + binary_macos_conda: + <<: *binary_common + macos: + xcode: "9.0" + steps: + - checkout + - install_build_tools_macos + - attach_workspace: + at: third_party + - run: + command: | + curl -o conda.sh https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh + sh conda.sh -b + source $HOME/miniconda3/bin/activate + conda install -yq conda-build + packaging/build_conda.sh + - store_artifacts: + path: /Users/distiller/miniconda3/conda-bld/osx-64 + - persist_to_workspace: + root: /Users/distiller/miniconda3/conda-bld/osx-64 + paths: + - "*" + + binary_windows_wheel: + <<: *binary_common + executor: + name: windows-cpu + steps: + - checkout + - run: + name: build + command: | + eval "$('/C/tools/miniconda3/Scripts/conda.exe' 'shell.bash' 'hook')" + conda activate base + bash packaging/build_wheel.sh + - store_artifacts: + path: dist + - persist_to_workspace: + root: dist + paths: + - "*" + + binary_windows_conda: + <<: *binary_common + executor: + name: windows-cpu + steps: + - checkout + - run: + name: build + command: | + eval "$('/C/tools/miniconda3/Scripts/conda.exe' 'shell.bash' 'hook')" + conda activate base + conda install -yq conda-build "conda-package-handling!=1.5.0" + bash packaging/build_conda.sh + - store_artifacts: + path: C:/tools/miniconda3/conda-bld/win-64 + - persist_to_workspace: + root: C:/tools/miniconda3/conda-bld/win-64 + paths: + - "*" + + # Requires org-member context + binary_conda_upload: + docker: + - image: continuumio/miniconda + steps: + - attach_workspace: + at: ~/workspace + - designate_upload_channel + - run: + command: | + # Prevent credential from leaking + conda install -yq anaconda-client + set -x + anaconda -t "${CONDA_PYTORCHBOT_TOKEN}" upload ~/workspace/*.tar.bz2 -u "pytorch-${UPLOAD_CHANNEL}" --label main --no-progress --force + + # Requires org-member context + binary_wheel_upload: + docker: + - image: circleci/python:3.8 + steps: + - attach_workspace: + at: ~/workspace + - checkout + - designate_upload_channel + - run: + command: | + pip install --user awscli + export PATH="$HOME/.local/bin:$PATH" + # Prevent credential from leaking + set +x + export AWS_ACCESS_KEY_ID="${PYTORCH_BINARY_AWS_ACCESS_KEY_ID}" + export AWS_SECRET_ACCESS_KEY="${PYTORCH_BINARY_AWS_SECRET_ACCESS_KEY}" + set -x + for pkg in ~/workspace/*.whl; do + aws s3 cp "$pkg" "s3://pytorch/whl/${UPLOAD_CHANNEL}/" --acl public-read + done + + smoke_test_linux_conda: + <<: *smoke_test_common + steps: + - attach_workspace: + at: ~/workspace + - run: + name: install binaries + command: | + set -x + source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION} + conda install -v -y -c pytorch-test -c pytorch-nightly pytorch + conda install -v -y $(ls ~/workspace/torchaudio*.tar.bz2) + - run: + name: smoke test + command: | + source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION} + python -c "import torchaudio" + + smoke_test_linux_pip: + <<: *smoke_test_common + steps: + - attach_workspace: + at: ~/workspace + - run: + name: install binaries + command: | + set -x + source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION} + pip install $(ls ~/workspace/torchaudio*.whl) -f https://download.pytorch.org/whl/nightly/torch_nightly.html -f https://download.pytorch.org/whl/test/torch_test.html + - run: + name: smoke test + command: | + source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION} + python -c "import torchaudio" + + smoke_test_windows_conda: + <<: *binary_common + executor: + name: windows-cpu + steps: + - attach_workspace: + at: ~/workspace + - run: + name: install binaries + command: | + set -x + eval "$('/C/tools/miniconda3/Scripts/conda.exe' 'shell.bash' 'hook')" + conda env remove -n python${PYTHON_VERSION} || true + conda create -yn python${PYTHON_VERSION} python=${PYTHON_VERSION} + conda activate python${PYTHON_VERSION} + conda install -v -y -c pytorch-test -c pytorch-nightly pytorch + conda install -v -y $(ls ~/workspace/torchaudio*.tar.bz2) + - run: + name: smoke test + command: | + eval "$('/C/tools/miniconda3/Scripts/conda.exe' 'shell.bash' 'hook')" + conda activate python${PYTHON_VERSION} + python -c "import torchaudio" + + smoke_test_windows_pip: + <<: *binary_common + executor: + name: windows-cpu + steps: + - attach_workspace: + at: ~/workspace + - run: + name: install binaries + command: | + set -x + eval "$('/C/tools/miniconda3/Scripts/conda.exe' 'shell.bash' 'hook')" + conda env remove -n python${PYTHON_VERSION} || true + conda create -yn python${PYTHON_VERSION} python=${PYTHON_VERSION} + conda activate python${PYTHON_VERSION} + pip install $(ls ~/workspace/torchaudio*.whl) -f https://download.pytorch.org/whl/nightly/torch_nightly.html -f https://download.pytorch.org/whl/test/torch_test.html + - run: + name: smoke test + command: | + eval "$('/C/tools/miniconda3/Scripts/conda.exe' 'shell.bash' 'hook')" + conda activate python${PYTHON_VERSION} + python -c "import torchaudio" + + smoke_test_docker_image_build: + machine: + image: ubuntu-1604:201903-01 + resource_class: large + environment: + image_name: torchaudio/smoke_test + steps: + - checkout + - run: + name: build_docker image + no_output_timeout: "1h" + command: | + cd .circleci/smoke_test/docker && docker build . -t ${image_name}:${CIRCLE_WORKFLOW_ID} + - run: + name: upload docker image + no_output_timeout: "1h" + command: | + set +x + export AWS_ACCESS_KEY_ID=${ECR_AWS_ACCESS_KEY} + export AWS_SECRET_ACCESS_KEY=${ECR_AWS_SECRET_ACCESS_KEY} + eval $(aws ecr get-login --region us-east-1 --no-include-email) + set -x + docker tag ${image_name}:${CIRCLE_WORKFLOW_ID} 308535385114.dkr.ecr.us-east-1.amazonaws.com/${image_name}:${CIRCLE_WORKFLOW_ID} + docker tag ${image_name}:${CIRCLE_WORKFLOW_ID} 308535385114.dkr.ecr.us-east-1.amazonaws.com/${image_name}:latest + docker push 308535385114.dkr.ecr.us-east-1.amazonaws.com/${image_name}:${CIRCLE_WORKFLOW_ID} + docker push 308535385114.dkr.ecr.us-east-1.amazonaws.com/${image_name}:latest + + unittest_linux_cpu: + <<: *binary_common + docker: + - image: "pytorch/torchaudio_unittest_base:manylinux" + resource_class: 2xlarge+ + steps: + - checkout + - attach_workspace: + at: third_party + - generate_cache_key + - restore_cache: + + keys: + - env-v3-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} + + - run: + name: Setup + command: .circleci/unittest/linux/scripts/setup_env.sh + - save_cache: + + key: env-v3-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} + + paths: + - conda + - env + - third_party/build + - third_party/install + - third_party/src + - run: + name: Install torchaudio + command: .circleci/unittest/linux/scripts/install.sh + - run: + name: Run tests + command: .circleci/unittest/linux/scripts/run_test.sh + - run: + name: Post process + command: .circleci/unittest/linux/scripts/post_process.sh + - store_test_results: + path: test-results + + unittest_linux_gpu: + <<: *binary_common + machine: + image: ubuntu-1604-cuda-10.1:201909-23 + resource_class: gpu.small + environment: + image_name: "pytorch/torchaudio_unittest_base:manylinux-cuda10.1" + steps: + - checkout + - attach_workspace: + at: third_party + - generate_cache_key + - restore_cache: + + keys: + - env-v3-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} + + - run: + name: Setup + command: docker run -t --gpus all -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux/scripts/setup_env.sh + - save_cache: + + key: env-v3-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} + + paths: + - conda + - env + - third_party/build + - third_party/install + - third_party/src + - run: + name: Install torchaudio + command: docker run -t --gpus all -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux/scripts/install.sh + - run: + name: Run tests + command: docker run -t --gpus all -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux/scripts/run_test.sh + - run: + name: Post Process + command: docker run -t --gpus all -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux/scripts/post_process.sh + - store_test_results: + path: test-results + + unittest_windows_cpu: + <<: *binary_common + executor: + name: windows-cpu + steps: + - checkout + - generate_cache_key + - restore_cache: + + keys: + - env-v3-windows-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/windows/scripts/environment.yml" }}-{{ checksum ".cachekey" }} + + - run: + name: Setup + command: .circleci/unittest/windows/scripts/setup_env.sh + - save_cache: + + key: env-v3-windows-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/windows/scripts/environment.yml" }}-{{ checksum ".cachekey" }} + + paths: + - conda + - env + - run: + name: Install torchaudio + command: .circleci/unittest/windows/scripts/install.sh + - run: + name: Run tests + command: .circleci/unittest/windows/scripts/run_test.sh + - run: + name: Post process + command: .circleci/unittest/windows/scripts/post_process.sh + - store_test_results: + path: test-results + + unittest_windows_gpu: + <<: *binary_common + executor: + name: windows-gpu + environment: + CUDA_VERSION: "10.1" + steps: + - checkout + - generate_cache_key + - restore_cache: + + keys: + - env-v1-windows-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/windows/scripts/environment.yml" }}-{{ checksum ".cachekey" }} + + - run: + name: Setup + command: .circleci/unittest/windows/scripts/setup_env.sh + - save_cache: + + key: env-v1-windows-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/windows/scripts/environment.yml" }}-{{ checksum ".cachekey" }} + + paths: + - conda + - env + - run: + name: Install torchaudio + command: .circleci/unittest/windows/scripts/install.sh + - run: + name: Run tests + command: .circleci/unittest/windows/scripts/run_test.sh + - run: + name: Post process + command: .circleci/unittest/windows/scripts/post_process.sh + - store_test_results: + path: test-results + + unittest_macos_cpu: + <<: *binary_common + macos: + xcode: "9.0" + resource_class: large + steps: + - checkout + - install_build_tools_macos + - attach_workspace: + at: third_party + - generate_cache_key + - restore_cache: + + keys: + - env-v3-macos-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} + + - run: + name: Setup + command: .circleci/unittest/linux/scripts/setup_env.sh + - save_cache: + + key: env-v3-macos-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} + + paths: + - conda + - env + - third_party/build + - third_party/install + - third_party/src + - run: + name: Install torchaudio + command: .circleci/unittest/linux/scripts/install.sh + - run: + name: Run tests + command: .circleci/unittest/linux/scripts/run_test.sh + - run: + name: Post process + command: .circleci/unittest/linux/scripts/post_process.sh + - store_test_results: + path: test-results + + stylecheck: + <<: *binary_common + docker: + - image: "pytorch/torchaudio_unittest_base:manylinux" + resource_class: medium + steps: + - checkout + - generate_cache_key + - restore_cache: + + keys: + - env-v2-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum ".cachekey" }} + + - run: + name: Setup + command: .circleci/unittest/linux/scripts/setup_env.sh + - save_cache: + + key: env-v2-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum ".cachekey" }} + + paths: + - conda + - env + - third_party/build + - third_party/install + - third_party/src + - run: + name: Run style check + command: .circleci/unittest/linux/scripts/run_style_checks.sh + +workflows: + build: + jobs: + - circleci_consistency + - download_third_parties_nix: + name: download_third_parties_nix + - binary_linux_wheel: + name: binary_linux_wheel_py3.6 + python_version: '3.6' + requires: + - download_third_parties_nix + - binary_linux_wheel: + name: binary_linux_wheel_py3.7 + python_version: '3.7' + requires: + - download_third_parties_nix + - binary_linux_wheel: + name: binary_linux_wheel_py3.8 + python_version: '3.8' + requires: + - download_third_parties_nix + - binary_macos_wheel: + name: binary_macos_wheel_py3.6 + python_version: '3.6' + requires: + - download_third_parties_nix + - binary_macos_wheel: + name: binary_macos_wheel_py3.7 + python_version: '3.7' + requires: + - download_third_parties_nix + - binary_macos_wheel: + name: binary_macos_wheel_py3.8 + python_version: '3.8' + requires: + - download_third_parties_nix + - binary_windows_wheel: + name: binary_windows_wheel_py3.6 + python_version: '3.6' + - binary_windows_wheel: + name: binary_windows_wheel_py3.7 + python_version: '3.7' + - binary_windows_wheel: + name: binary_windows_wheel_py3.8 + python_version: '3.8' + - binary_linux_conda: + name: binary_linux_conda_py3.6 + python_version: '3.6' + requires: + - download_third_parties_nix + - binary_linux_conda: + name: binary_linux_conda_py3.7 + python_version: '3.7' + requires: + - download_third_parties_nix + - binary_linux_conda: + name: binary_linux_conda_py3.8 + python_version: '3.8' + requires: + - download_third_parties_nix + - binary_macos_conda: + name: binary_macos_conda_py3.6 + python_version: '3.6' + requires: + - download_third_parties_nix + - binary_macos_conda: + name: binary_macos_conda_py3.7 + python_version: '3.7' + requires: + - download_third_parties_nix + - binary_macos_conda: + name: binary_macos_conda_py3.8 + python_version: '3.8' + requires: + - download_third_parties_nix + - binary_windows_conda: + name: binary_windows_conda_py3.6 + python_version: '3.6' + - binary_windows_conda: + name: binary_windows_conda_py3.7 + python_version: '3.7' + - binary_windows_conda: + name: binary_windows_conda_py3.8 + python_version: '3.8' + unittest: + jobs: + - download_third_parties_nix: + name: download_third_parties_nix + - unittest_linux_cpu: + name: unittest_linux_cpu_py3.6 + python_version: '3.6' + requires: + - download_third_parties_nix + - stylecheck: + name: stylecheck_py3.6 + python_version: '3.6' + - unittest_linux_cpu: + name: unittest_linux_cpu_py3.7 + python_version: '3.7' + requires: + - download_third_parties_nix + - unittest_linux_cpu: + name: unittest_linux_cpu_py3.8 + python_version: '3.8' + requires: + - download_third_parties_nix + - unittest_linux_gpu: + filters: + branches: + only: + - master + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: unittest_linux_gpu_py3.6 + python_version: '3.6' + requires: + - download_third_parties_nix + - unittest_linux_gpu: + filters: + branches: + only: + - master + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: unittest_linux_gpu_py3.7 + python_version: '3.7' + requires: + - download_third_parties_nix + - unittest_linux_gpu: + filters: + branches: + only: + - master + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: unittest_linux_gpu_py3.8 + python_version: '3.8' + requires: + - download_third_parties_nix + - unittest_windows_cpu: + name: unittest_windows_cpu_py3.6 + python_version: '3.6' + - unittest_windows_cpu: + name: unittest_windows_cpu_py3.7 + python_version: '3.7' + - unittest_windows_cpu: + name: unittest_windows_cpu_py3.8 + python_version: '3.8' + - unittest_windows_gpu: + filters: + branches: + only: + - master + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: unittest_windows_gpu_py3.6 + python_version: '3.6' + - unittest_windows_gpu: + filters: + branches: + only: + - master + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: unittest_windows_gpu_py3.7 + python_version: '3.7' + - unittest_windows_gpu: + filters: + branches: + only: + - master + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: unittest_windows_gpu_py3.8 + python_version: '3.8' + - unittest_macos_cpu: + name: unittest_macos_cpu_py3.6 + python_version: '3.6' + requires: + - download_third_parties_nix + - unittest_macos_cpu: + name: unittest_macos_cpu_py3.7 + python_version: '3.7' + requires: + - download_third_parties_nix + - unittest_macos_cpu: + name: unittest_macos_cpu_py3.8 + python_version: '3.8' + requires: + - download_third_parties_nix + nightly: + jobs: + - circleci_consistency: + filters: + branches: + only: nightly + - download_third_parties_nix: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: download_third_parties_nix + - binary_linux_wheel: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_wheel_py3.6 + python_version: '3.6' + requires: + - download_third_parties_nix + - binary_wheel_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_wheel_py3.6_upload + requires: + - nightly_binary_linux_wheel_py3.6 + - smoke_test_linux_pip: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_wheel_py3.6_smoke_test_pip + python_version: '3.6' + requires: + - nightly_binary_linux_wheel_py3.6_upload + - binary_linux_wheel: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_wheel_py3.7 + python_version: '3.7' + requires: + - download_third_parties_nix + - binary_wheel_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_wheel_py3.7_upload + requires: + - nightly_binary_linux_wheel_py3.7 + - smoke_test_linux_pip: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_wheel_py3.7_smoke_test_pip + python_version: '3.7' + requires: + - nightly_binary_linux_wheel_py3.7_upload + - binary_linux_wheel: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_wheel_py3.8 + python_version: '3.8' + requires: + - download_third_parties_nix + - binary_wheel_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_wheel_py3.8_upload + requires: + - nightly_binary_linux_wheel_py3.8 + - smoke_test_linux_pip: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_wheel_py3.8_smoke_test_pip + python_version: '3.8' + requires: + - nightly_binary_linux_wheel_py3.8_upload + - binary_macos_wheel: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_macos_wheel_py3.6 + python_version: '3.6' + requires: + - download_third_parties_nix + - binary_wheel_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_macos_wheel_py3.6_upload + requires: + - nightly_binary_macos_wheel_py3.6 + - binary_macos_wheel: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_macos_wheel_py3.7 + python_version: '3.7' + requires: + - download_third_parties_nix + - binary_wheel_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_macos_wheel_py3.7_upload + requires: + - nightly_binary_macos_wheel_py3.7 + - binary_macos_wheel: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_macos_wheel_py3.8 + python_version: '3.8' + requires: + - download_third_parties_nix + - binary_wheel_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_macos_wheel_py3.8_upload + requires: + - nightly_binary_macos_wheel_py3.8 + - binary_windows_wheel: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_wheel_py3.6 + python_version: '3.6' + - binary_wheel_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_wheel_py3.6_upload + requires: + - nightly_binary_windows_wheel_py3.6 + - smoke_test_windows_pip: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_wheel_py3.6_smoke_test_pip + python_version: '3.6' + requires: + - nightly_binary_windows_wheel_py3.6_upload + - binary_windows_wheel: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_wheel_py3.7 + python_version: '3.7' + - binary_wheel_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_wheel_py3.7_upload + requires: + - nightly_binary_windows_wheel_py3.7 + - smoke_test_windows_pip: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_wheel_py3.7_smoke_test_pip + python_version: '3.7' + requires: + - nightly_binary_windows_wheel_py3.7_upload + - binary_windows_wheel: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_wheel_py3.8 + python_version: '3.8' + - binary_wheel_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_wheel_py3.8_upload + requires: + - nightly_binary_windows_wheel_py3.8 + - smoke_test_windows_pip: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_wheel_py3.8_smoke_test_pip + python_version: '3.8' + requires: + - nightly_binary_windows_wheel_py3.8_upload + - binary_linux_conda: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_conda_py3.6 + python_version: '3.6' + requires: + - download_third_parties_nix + - binary_conda_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_conda_py3.6_upload + requires: + - nightly_binary_linux_conda_py3.6 + - smoke_test_linux_conda: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_conda_py3.6_smoke_test_conda + python_version: '3.6' + requires: + - nightly_binary_linux_conda_py3.6_upload + - binary_linux_conda: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_conda_py3.7 + python_version: '3.7' + requires: + - download_third_parties_nix + - binary_conda_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_conda_py3.7_upload + requires: + - nightly_binary_linux_conda_py3.7 + - smoke_test_linux_conda: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_conda_py3.7_smoke_test_conda + python_version: '3.7' + requires: + - nightly_binary_linux_conda_py3.7_upload + - binary_linux_conda: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_conda_py3.8 + python_version: '3.8' + requires: + - download_third_parties_nix + - binary_conda_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_conda_py3.8_upload + requires: + - nightly_binary_linux_conda_py3.8 + - smoke_test_linux_conda: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_linux_conda_py3.8_smoke_test_conda + python_version: '3.8' + requires: + - nightly_binary_linux_conda_py3.8_upload + - binary_macos_conda: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_macos_conda_py3.6 + python_version: '3.6' + requires: + - download_third_parties_nix + - binary_conda_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_macos_conda_py3.6_upload + requires: + - nightly_binary_macos_conda_py3.6 + - binary_macos_conda: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_macos_conda_py3.7 + python_version: '3.7' + requires: + - download_third_parties_nix + - binary_conda_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_macos_conda_py3.7_upload + requires: + - nightly_binary_macos_conda_py3.7 + - binary_macos_conda: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_macos_conda_py3.8 + python_version: '3.8' + requires: + - download_third_parties_nix + - binary_conda_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_macos_conda_py3.8_upload + requires: + - nightly_binary_macos_conda_py3.8 + - binary_windows_conda: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_conda_py3.6 + python_version: '3.6' + - binary_conda_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_conda_py3.6_upload + requires: + - nightly_binary_windows_conda_py3.6 + - smoke_test_windows_conda: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_conda_py3.6_smoke_test_conda + python_version: '3.6' + requires: + - nightly_binary_windows_conda_py3.6_upload + - binary_windows_conda: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_conda_py3.7 + python_version: '3.7' + - binary_conda_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_conda_py3.7_upload + requires: + - nightly_binary_windows_conda_py3.7 + - smoke_test_windows_conda: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_conda_py3.7_smoke_test_conda + python_version: '3.7' + requires: + - nightly_binary_windows_conda_py3.7_upload + - binary_windows_conda: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_conda_py3.8 + python_version: '3.8' + - binary_conda_upload: + context: org-member + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_conda_py3.8_upload + requires: + - nightly_binary_windows_conda_py3.8 + - smoke_test_windows_conda: + filters: + branches: + only: + - nightly + tags: + only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/ + name: nightly_binary_windows_conda_py3.8_smoke_test_conda + python_version: '3.8' + requires: + - nightly_binary_windows_conda_py3.8_upload + docker_build: + triggers: + - schedule: + cron: "0 10 * * 0" + filters: + branches: + only: + - master + jobs: + - smoke_test_docker_image_build \ No newline at end of file diff --git a/.circleci/config.yml.in b/.circleci/config.yml.in new file mode 100644 index 0000000000..6d6d77d50f --- /dev/null +++ b/.circleci/config.yml.in @@ -0,0 +1,631 @@ +version: 2.1 + +# How to test the Linux jobs: +# - Install CircleCI local CLI: https://circleci.com/docs/2.0/local-cli/ +# - circleci config process .circleci/config.yml > gen.yml && circleci local execute -c gen.yml --job binary_linux_wheel_py3.8 +# - Replace binary_linux_wheel_py3.8 with the name of the job you want to test. +# Job names are 'name:' key. + +executors: + windows-cpu: + machine: + resource_class: windows.xlarge + image: windows-server-2019-vs2019:stable + shell: bash.exe + + windows-gpu: + machine: + resource_class: windows.gpu.nvidia.medium + image: windows-server-2019-nvidia:stable + shell: bash.exe + +commands: + generate_cache_key: + description: "Generates a cache key file that changes daily" + steps: + - run: + name: Generate cache key + command: echo "$(date +"%Y-%m-%d")" > .cachekey + designate_upload_channel: + description: "inserts the correct upload channel into ${BASH_ENV}" + steps: + - run: + name: adding UPLOAD_CHANNEL to BASH_ENV + command: | + our_upload_channel=nightly + # On tags upload to test instead + if [[ -n "${CIRCLE_TAG}" ]]; then + our_upload_channel=test + fi + echo "export UPLOAD_CHANNEL=${our_upload_channel}" >> ${BASH_ENV} + install_build_tools_macos: + description: "installs tools required to build torchaudio" + steps: + - run: + name: Install cmake and pkg-config + command: HOMEBREW_NO_AUTO_UPDATE=1 brew install cmake pkg-config wget + # Disable brew auto update which is very slow + +binary_common: &binary_common + parameters: + # Edit these defaults to do a release + build_version: + description: "version number of release binary; by default, build a nightly" + type: string + default: "" + pytorch_version: + description: "PyTorch version to build against; by default, use a nightly" + type: string + default: "" + # Don't edit these + python_version: + description: "Python version to build against (e.g., 3.8)" + type: string + environment: + PYTHON_VERSION: << parameters.python_version >> + BUILD_VERSION: << parameters.build_version >> + PYTORCH_VERSION: << parameters.pytorch_version >> + CU_VERSION: cpu + +smoke_test_common: &smoke_test_common + <<: *binary_common + docker: + - image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/torchaudio/smoke_test:56c846a5-acaa-41a7-92f5-46ec66186c61 + aws_auth: + aws_access_key_id: ${ECR_AWS_ACCESS_KEY} + aws_secret_access_key: ${ECR_AWS_SECRET_ACCESS_KEY} + +jobs: + circleci_consistency: + docker: + - image: circleci/python:3.8 + steps: + - checkout + - run: + command: | + pip install --user --progress-bar off jinja2 pyyaml + python .circleci/regenerate.py + git diff --exit-code || (echo ".circleci/config.yml not in sync with config.yml.in! Run .circleci/regenerate.py to update config"; exit 1) + + download_third_parties_nix: + docker: + - image: "pytorch/torchaudio_unittest_base:manylinux" + resource_class: small + steps: + - checkout + - generate_cache_key + - restore_cache: + {% raw %} + keys: + - tp-nix-v2-{{ checksum ".cachekey" }} + {% endraw %} + - run: + command: | + mkdir -p third_party/archives/ + wget --no-clobber --directory-prefix=third_party/archives/ $(awk '/URL /{print $2}' third_party/CMakeLists.txt) + - save_cache: + {% raw %} + key: tp-nix-v2-{{ checksum ".cachekey" }} + {% endraw %} + paths: + - third_party/archives + - persist_to_workspace: + root: third_party + paths: + - archives + + binary_linux_wheel: + <<: *binary_common + docker: + - image: "pytorch/manylinux-cuda100" + resource_class: 2xlarge+ + steps: + - checkout + - attach_workspace: + at: third_party + - run: packaging/build_wheel.sh + - store_artifacts: + path: dist + - persist_to_workspace: + root: dist + paths: + - "*" + + binary_linux_conda: + <<: *binary_common + docker: + - image: "pytorch/conda-cuda" + resource_class: 2xlarge+ + steps: + - checkout + - attach_workspace: + at: third_party + - run: packaging/build_conda.sh + - store_artifacts: + path: /opt/conda/conda-bld/linux-64 + - persist_to_workspace: + root: /opt/conda/conda-bld/linux-64 + paths: + - "*" + + binary_macos_wheel: + <<: *binary_common + macos: + xcode: "9.0" + steps: + - checkout + - install_build_tools_macos + - attach_workspace: + at: third_party + - run: + # Cannot easily deduplicate this as source'ing activate + # will set environment variables which we need to propagate + # to build_wheel.sh + command: | + curl -o conda.sh https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh + sh conda.sh -b + source $HOME/miniconda3/bin/activate + packaging/build_wheel.sh + - store_artifacts: + path: dist + - persist_to_workspace: + root: dist + paths: + - "*" + + binary_macos_conda: + <<: *binary_common + macos: + xcode: "9.0" + steps: + - checkout + - install_build_tools_macos + - attach_workspace: + at: third_party + - run: + command: | + curl -o conda.sh https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh + sh conda.sh -b + source $HOME/miniconda3/bin/activate + conda install -yq conda-build + packaging/build_conda.sh + - store_artifacts: + path: /Users/distiller/miniconda3/conda-bld/osx-64 + - persist_to_workspace: + root: /Users/distiller/miniconda3/conda-bld/osx-64 + paths: + - "*" + + binary_windows_wheel: + <<: *binary_common + executor: + name: windows-cpu + steps: + - checkout + - run: + name: build + command: | + eval "$('/C/tools/miniconda3/Scripts/conda.exe' 'shell.bash' 'hook')" + conda activate base + bash packaging/build_wheel.sh + - store_artifacts: + path: dist + - persist_to_workspace: + root: dist + paths: + - "*" + + binary_windows_conda: + <<: *binary_common + executor: + name: windows-cpu + steps: + - checkout + - run: + name: build + command: | + eval "$('/C/tools/miniconda3/Scripts/conda.exe' 'shell.bash' 'hook')" + conda activate base + conda install -yq conda-build "conda-package-handling!=1.5.0" + bash packaging/build_conda.sh + - store_artifacts: + path: C:/tools/miniconda3/conda-bld/win-64 + - persist_to_workspace: + root: C:/tools/miniconda3/conda-bld/win-64 + paths: + - "*" + + # Requires org-member context + binary_conda_upload: + docker: + - image: continuumio/miniconda + steps: + - attach_workspace: + at: ~/workspace + - designate_upload_channel + - run: + command: | + # Prevent credential from leaking + conda install -yq anaconda-client + set -x + anaconda -t "${CONDA_PYTORCHBOT_TOKEN}" upload ~/workspace/*.tar.bz2 -u "pytorch-${UPLOAD_CHANNEL}" --label main --no-progress --force + + # Requires org-member context + binary_wheel_upload: + docker: + - image: circleci/python:3.8 + steps: + - attach_workspace: + at: ~/workspace + - checkout + - designate_upload_channel + - run: + command: | + pip install --user awscli + export PATH="$HOME/.local/bin:$PATH" + # Prevent credential from leaking + set +x + export AWS_ACCESS_KEY_ID="${PYTORCH_BINARY_AWS_ACCESS_KEY_ID}" + export AWS_SECRET_ACCESS_KEY="${PYTORCH_BINARY_AWS_SECRET_ACCESS_KEY}" + set -x + for pkg in ~/workspace/*.whl; do + aws s3 cp "$pkg" "s3://pytorch/whl/${UPLOAD_CHANNEL}/" --acl public-read + done + + smoke_test_linux_conda: + <<: *smoke_test_common + steps: + - attach_workspace: + at: ~/workspace + - run: + name: install binaries + command: | + set -x + source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION} + conda install -v -y -c pytorch-test -c pytorch-nightly pytorch + conda install -v -y $(ls ~/workspace/torchaudio*.tar.bz2) + - run: + name: smoke test + command: | + source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION} + python -c "import torchaudio" + + smoke_test_linux_pip: + <<: *smoke_test_common + steps: + - attach_workspace: + at: ~/workspace + - run: + name: install binaries + command: | + set -x + source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION} + pip install $(ls ~/workspace/torchaudio*.whl) -f https://download.pytorch.org/whl/nightly/torch_nightly.html -f https://download.pytorch.org/whl/test/torch_test.html + - run: + name: smoke test + command: | + source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION} + python -c "import torchaudio" + + smoke_test_windows_conda: + <<: *binary_common + executor: + name: windows-cpu + steps: + - attach_workspace: + at: ~/workspace + - run: + name: install binaries + command: | + set -x + eval "$('/C/tools/miniconda3/Scripts/conda.exe' 'shell.bash' 'hook')" + conda env remove -n python${PYTHON_VERSION} || true + conda create -yn python${PYTHON_VERSION} python=${PYTHON_VERSION} + conda activate python${PYTHON_VERSION} + conda install -v -y -c pytorch-test -c pytorch-nightly pytorch + conda install -v -y $(ls ~/workspace/torchaudio*.tar.bz2) + - run: + name: smoke test + command: | + eval "$('/C/tools/miniconda3/Scripts/conda.exe' 'shell.bash' 'hook')" + conda activate python${PYTHON_VERSION} + python -c "import torchaudio" + + smoke_test_windows_pip: + <<: *binary_common + executor: + name: windows-cpu + steps: + - attach_workspace: + at: ~/workspace + - run: + name: install binaries + command: | + set -x + eval "$('/C/tools/miniconda3/Scripts/conda.exe' 'shell.bash' 'hook')" + conda env remove -n python${PYTHON_VERSION} || true + conda create -yn python${PYTHON_VERSION} python=${PYTHON_VERSION} + conda activate python${PYTHON_VERSION} + pip install $(ls ~/workspace/torchaudio*.whl) -f https://download.pytorch.org/whl/nightly/torch_nightly.html -f https://download.pytorch.org/whl/test/torch_test.html + - run: + name: smoke test + command: | + eval "$('/C/tools/miniconda3/Scripts/conda.exe' 'shell.bash' 'hook')" + conda activate python${PYTHON_VERSION} + python -c "import torchaudio" + + smoke_test_docker_image_build: + machine: + image: ubuntu-1604:201903-01 + resource_class: large + environment: + image_name: torchaudio/smoke_test + steps: + - checkout + - run: + name: build_docker image + no_output_timeout: "1h" + command: | + cd .circleci/smoke_test/docker && docker build . -t ${image_name}:${CIRCLE_WORKFLOW_ID} + - run: + name: upload docker image + no_output_timeout: "1h" + command: | + set +x + export AWS_ACCESS_KEY_ID=${ECR_AWS_ACCESS_KEY} + export AWS_SECRET_ACCESS_KEY=${ECR_AWS_SECRET_ACCESS_KEY} + eval $(aws ecr get-login --region us-east-1 --no-include-email) + set -x + docker tag ${image_name}:${CIRCLE_WORKFLOW_ID} 308535385114.dkr.ecr.us-east-1.amazonaws.com/${image_name}:${CIRCLE_WORKFLOW_ID} + docker tag ${image_name}:${CIRCLE_WORKFLOW_ID} 308535385114.dkr.ecr.us-east-1.amazonaws.com/${image_name}:latest + docker push 308535385114.dkr.ecr.us-east-1.amazonaws.com/${image_name}:${CIRCLE_WORKFLOW_ID} + docker push 308535385114.dkr.ecr.us-east-1.amazonaws.com/${image_name}:latest + + unittest_linux_cpu: + <<: *binary_common + docker: + - image: "pytorch/torchaudio_unittest_base:manylinux" + resource_class: 2xlarge+ + steps: + - checkout + - attach_workspace: + at: third_party + - generate_cache_key + - restore_cache: + {% raw %} + keys: + - env-v3-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} + {% endraw %} + - run: + name: Setup + command: .circleci/unittest/linux/scripts/setup_env.sh + - save_cache: + {% raw %} + key: env-v3-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} + {% endraw %} + paths: + - conda + - env + - third_party/build + - third_party/install + - third_party/src + - run: + name: Install torchaudio + command: .circleci/unittest/linux/scripts/install.sh + - run: + name: Run tests + command: .circleci/unittest/linux/scripts/run_test.sh + - run: + name: Post process + command: .circleci/unittest/linux/scripts/post_process.sh + - store_test_results: + path: test-results + + unittest_linux_gpu: + <<: *binary_common + machine: + image: ubuntu-1604-cuda-10.1:201909-23 + resource_class: gpu.small + environment: + image_name: "pytorch/torchaudio_unittest_base:manylinux-cuda10.1" + steps: + - checkout + - attach_workspace: + at: third_party + - generate_cache_key + - restore_cache: + {% raw %} + keys: + - env-v3-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} + {% endraw %} + - run: + name: Setup + command: docker run -t --gpus all -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux/scripts/setup_env.sh + - save_cache: + {% raw %} + key: env-v3-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} + {% endraw %} + paths: + - conda + - env + - third_party/build + - third_party/install + - third_party/src + - run: + name: Install torchaudio + command: docker run -t --gpus all -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux/scripts/install.sh + - run: + name: Run tests + command: docker run -t --gpus all -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux/scripts/run_test.sh + - run: + name: Post Process + command: docker run -t --gpus all -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux/scripts/post_process.sh + - store_test_results: + path: test-results + + unittest_windows_cpu: + <<: *binary_common + executor: + name: windows-cpu + steps: + - checkout + - generate_cache_key + - restore_cache: + {% raw %} + keys: + - env-v3-windows-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/windows/scripts/environment.yml" }}-{{ checksum ".cachekey" }} + {% endraw %} + - run: + name: Setup + command: .circleci/unittest/windows/scripts/setup_env.sh + - save_cache: + {% raw %} + key: env-v3-windows-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/windows/scripts/environment.yml" }}-{{ checksum ".cachekey" }} + {% endraw %} + paths: + - conda + - env + - run: + name: Install torchaudio + command: .circleci/unittest/windows/scripts/install.sh + - run: + name: Run tests + command: .circleci/unittest/windows/scripts/run_test.sh + - run: + name: Post process + command: .circleci/unittest/windows/scripts/post_process.sh + - store_test_results: + path: test-results + + unittest_windows_gpu: + <<: *binary_common + executor: + name: windows-gpu + environment: + CUDA_VERSION: "10.1" + steps: + - checkout + - generate_cache_key + - restore_cache: + {% raw %} + keys: + - env-v1-windows-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/windows/scripts/environment.yml" }}-{{ checksum ".cachekey" }} + {% endraw %} + - run: + name: Setup + command: .circleci/unittest/windows/scripts/setup_env.sh + - save_cache: + {% raw %} + key: env-v1-windows-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/windows/scripts/environment.yml" }}-{{ checksum ".cachekey" }} + {% endraw %} + paths: + - conda + - env + - run: + name: Install torchaudio + command: .circleci/unittest/windows/scripts/install.sh + - run: + name: Run tests + command: .circleci/unittest/windows/scripts/run_test.sh + - run: + name: Post process + command: .circleci/unittest/windows/scripts/post_process.sh + - store_test_results: + path: test-results + + unittest_macos_cpu: + <<: *binary_common + macos: + xcode: "9.0" + resource_class: large + steps: + - checkout + - install_build_tools_macos + - attach_workspace: + at: third_party + - generate_cache_key + - restore_cache: + {% raw %} + keys: + - env-v3-macos-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} + {% endraw %} + - run: + name: Setup + command: .circleci/unittest/linux/scripts/setup_env.sh + - save_cache: + {% raw %} + key: env-v3-macos-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum "third_party/CMakeLists.txt" }}-{{ checksum ".cachekey" }} + {% endraw %} + paths: + - conda + - env + - third_party/build + - third_party/install + - third_party/src + - run: + name: Install torchaudio + command: .circleci/unittest/linux/scripts/install.sh + - run: + name: Run tests + command: .circleci/unittest/linux/scripts/run_test.sh + - run: + name: Post process + command: .circleci/unittest/linux/scripts/post_process.sh + - store_test_results: + path: test-results + + stylecheck: + <<: *binary_common + docker: + - image: "pytorch/torchaudio_unittest_base:manylinux" + resource_class: medium + steps: + - checkout + - generate_cache_key + - restore_cache: + {% raw %} + keys: + - env-v2-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum ".cachekey" }} + {% endraw %} + - run: + name: Setup + command: .circleci/unittest/linux/scripts/setup_env.sh + - save_cache: + {% raw %} + key: env-v2-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum ".cachekey" }} + {% endraw %} + paths: + - conda + - env + - third_party/build + - third_party/install + - third_party/src + - run: + name: Run style check + command: .circleci/unittest/linux/scripts/run_style_checks.sh + +workflows: + build: + jobs: + - circleci_consistency + {{ build_workflows() }} + unittest: + jobs: + {{ unittest_workflows() }} + nightly: + jobs: + - circleci_consistency: + filters: + branches: + only: nightly + {{ build_workflows(prefix="nightly_", filter_branch="nightly", upload=True) }} + docker_build: + triggers: + - schedule: + cron: "0 10 * * 0" + filters: + branches: + only: + - master + jobs: + - smoke_test_docker_image_build diff --git a/.circleci/regenerate.py b/.circleci/regenerate.py new file mode 100755 index 0000000000..2c9fe7ee4f --- /dev/null +++ b/.circleci/regenerate.py @@ -0,0 +1,180 @@ +#!/usr/bin/env python3 + +""" +This script should use a very simple, functional programming style. +Avoid Jinja macros in favor of native Python functions. + +Don't go overboard on code generation; use Python only to generate +content that can't be easily declared statically using CircleCI's YAML API. + +Data declarations (e.g. the nested loops for defining the configuration matrix) +should be at the top of the file for easy updating. + +See this comment for design rationale: +https://github.com/pytorch/vision/pull/1321#issuecomment-531033978 +""" + +import jinja2 +import yaml +import os.path + + +PYTHON_VERSIONS = ["3.6", "3.7", "3.8"] + + +def build_workflows(prefix='', upload=False, filter_branch=None, indentation=6): + w = [] + w += build_download_job(filter_branch) + for btype in ["wheel", "conda"]: + for os_type in ["linux", "macos", "windows"]: + for python_version in PYTHON_VERSIONS: + w += build_workflow_pair(btype, os_type, python_version, filter_branch, prefix, upload) + + return indent(indentation, w) + + +def build_download_job(filter_branch): + job = { + "name": "download_third_parties_nix", + } + + if filter_branch: + job["filters"] = gen_filter_branch_tree(filter_branch) + return [{"download_third_parties_nix": job}] + + +def build_workflow_pair(btype, os_type, python_version, filter_branch, prefix='', upload=False): + + w = [] + base_workflow_name = "{prefix}binary_{os_type}_{btype}_py{python_version}".format( + prefix=prefix, + os_type=os_type, + btype=btype, + python_version=python_version, + ) + + w.append(generate_base_workflow(base_workflow_name, python_version, filter_branch, os_type, btype)) + + if upload: + + is_py3_linux = os_type in ['linux', "windows"] and not python_version.startswith("2.") + + w.append(generate_upload_workflow(base_workflow_name, filter_branch, btype)) + + if filter_branch == 'nightly' and is_py3_linux: + pydistro = 'pip' if btype == 'wheel' else 'conda' + w.append(generate_smoketest_workflow(pydistro, base_workflow_name, filter_branch, python_version, os_type)) + + return w + + +def generate_base_workflow(base_workflow_name, python_version, filter_branch, os_type, btype): + + d = { + "name": base_workflow_name, + "python_version": python_version, + } + + if os_type in ['linux', 'macos']: + d['requires'] = ['download_third_parties_nix'] + + if filter_branch: + d["filters"] = gen_filter_branch_tree(filter_branch) + + return {"binary_{os_type}_{btype}".format(os_type=os_type, btype=btype): d} + + +def gen_filter_branch_tree(*branches): + return { + "branches": { + "only": list(branches), + }, + "tags": { + # Using a raw string here to avoid having to escape + # anything + "only": r"/v[0-9]+(\.[0-9]+)*-rc[0-9]+/" + } + } + + +def generate_upload_workflow(base_workflow_name, filter_branch, btype): + d = { + "name": "{base_workflow_name}_upload".format(base_workflow_name=base_workflow_name), + "context": "org-member", + "requires": [base_workflow_name], + } + + if filter_branch: + d["filters"] = gen_filter_branch_tree(filter_branch) + + return {"binary_{btype}_upload".format(btype=btype): d} + + +def generate_smoketest_workflow(pydistro, base_workflow_name, filter_branch, python_version, os_type): + + required_build_suffix = "_upload" + required_build_name = base_workflow_name + required_build_suffix + + smoke_suffix = "smoke_test_{pydistro}".format(pydistro=pydistro) + d = { + "name": "{base_workflow_name}_{smoke_suffix}".format( + base_workflow_name=base_workflow_name, smoke_suffix=smoke_suffix), + "requires": [required_build_name], + "python_version": python_version, + } + + if filter_branch: + d["filters"] = gen_filter_branch_tree(filter_branch) + + return {"smoke_test_{os_type}_{pydistro}".format(os_type=os_type, pydistro=pydistro): d} + + +def indent(indentation, data_list): + return ("\n" + " " * indentation).join(yaml.dump(data_list).splitlines()) + + +def unittest_workflows(indentation=6): + jobs = [] + jobs += build_download_job(None) + for os_type in ["linux", "windows", "macos"]: + for device_type in ["cpu", "gpu"]: + if os_type == "macos" and device_type == "gpu": + continue + + for i, python_version in enumerate(PYTHON_VERSIONS): + job = { + "name": f"unittest_{os_type}_{device_type}_py{python_version}", + "python_version": python_version, + } + + if device_type == 'gpu': + job['filters'] = gen_filter_branch_tree('master', 'nightly') + + if os_type != "windows": + job['requires'] = ['download_third_parties_nix'] + + jobs.append({f"unittest_{os_type}_{device_type}": job}) + + if i == 0 and os_type == "linux" and device_type == "cpu": + jobs.append({ + f"stylecheck": { + "name": f"stylecheck_py{python_version}", + "python_version": python_version, + } + }) + return indent(indentation, jobs) + + +if __name__ == "__main__": + d = os.path.dirname(__file__) + env = jinja2.Environment( + loader=jinja2.FileSystemLoader(d), + lstrip_blocks=True, + autoescape=False, + ) + + with open(os.path.join(d, 'config.yml'), 'w') as f: + f.write(env.get_template('config.yml.in').render( + build_workflows=build_workflows, + unittest_workflows=unittest_workflows, + )) diff --git a/.circleci/smoke_test/docker/Dockerfile b/.circleci/smoke_test/docker/Dockerfile new file mode 100644 index 0000000000..acfac51fcd --- /dev/null +++ b/.circleci/smoke_test/docker/Dockerfile @@ -0,0 +1,36 @@ +# this Dockerfile is for torchaudio smoke test, it will be created periodically via CI system +# if you need to do it locally, follow below steps once you have Docker installed +# assuming you're within the directory where this Dockerfile located +# $ docker build . -t torchaudio/smoketest + +# if you want to push to aws ecr, make sure you have the rights to write to ECR, then run +# $ eval $(aws ecr get-login --region us-east-1 --no-include-email) +# $ export MYTAG=localbuild ## you can choose whatever tag you like +# $ docker tag torchaudio/smoketest 308535385114.dkr.ecr.us-east-1.amazonaws.com/torchaudio/smoke_test:${MYTAG} +# $ docker push 308535385114.dkr.ecr.us-east-1.amazonaws.com/torchaudio/smoke_test:${MYTAG} + +FROM ubuntu:latest + +RUN apt-get -qq update && apt-get -qq -y install curl bzip2 sox libsox-dev libsox-fmt-all \ + && curl -sSL https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -o /tmp/miniconda.sh \ + && bash /tmp/miniconda.sh -bfp /usr/local \ + && rm -rf /tmp/miniconda.sh \ + && conda install -y python=3 \ + && conda update conda \ + && apt-get -qq -y remove curl bzip2 \ + && apt-get -qq -y autoremove \ + && apt-get autoclean \ + && rm -rf /var/lib/apt/lists/* /var/log/dpkg.log \ + && conda clean --all --yes + +ENV PATH /opt/conda/bin:$PATH + +RUN conda create -y --name python3.6 python=3.6 +RUN conda create -y --name python3.7 python=3.7 +RUN conda create -y --name python3.8 python=3.8 +SHELL [ "/bin/bash", "-c" ] +RUN echo "source /usr/local/etc/profile.d/conda.sh" >> ~/.bashrc +RUN source /usr/local/etc/profile.d/conda.sh && conda activate python3.6 && conda install -y -c conda-forge sox && conda install -y numpy +RUN source /usr/local/etc/profile.d/conda.sh && conda activate python3.7 && conda install -y -c conda-forge sox && conda install -y numpy +RUN source /usr/local/etc/profile.d/conda.sh && conda activate python3.8 && conda install -y -c conda-forge sox && conda install -y numpy +CMD [ "/bin/bash"] diff --git a/.circleci/test/test_sort_yaml.py b/.circleci/test/test_sort_yaml.py new file mode 100755 index 0000000000..dc6db481dd --- /dev/null +++ b/.circleci/test/test_sort_yaml.py @@ -0,0 +1,14 @@ +#!/usr/bin/env python3 + +""" +To compare new version with previous: + + ./regenerate.sh + meld <(git show HEAD:./config.yml | ./sort-yaml.py) <(cat config.yml | ./sort-yaml.py) +""" + + +import sys +import yaml + +sys.stdout.write(yaml.dump(yaml.load(sys.stdin, Loader=yaml.FullLoader), sort_keys=True)) diff --git a/.circleci/unittest/linux/README.md b/.circleci/unittest/linux/README.md new file mode 100644 index 0000000000..0a4b0e0e63 --- /dev/null +++ b/.circleci/unittest/linux/README.md @@ -0,0 +1,6 @@ +This directory contains; + + - docker + Docker image definition and scripts to build and update Docker image for unittest. + - scripts + Scripts used by CircleCI to run unit tests. diff --git a/.circleci/unittest/linux/docker/.dockerignore b/.circleci/unittest/linux/docker/.dockerignore new file mode 100644 index 0000000000..1398d409f8 --- /dev/null +++ b/.circleci/unittest/linux/docker/.dockerignore @@ -0,0 +1,2 @@ +* +!scripts diff --git a/.circleci/unittest/linux/docker/.gitignore b/.circleci/unittest/linux/docker/.gitignore new file mode 100644 index 0000000000..7e977058dd --- /dev/null +++ b/.circleci/unittest/linux/docker/.gitignore @@ -0,0 +1,2 @@ +scripts/build_third_parties.sh +Dockerfile.tmp diff --git a/.circleci/unittest/linux/docker/Dockerfile b/.circleci/unittest/linux/docker/Dockerfile new file mode 100644 index 0000000000..d91b5222a4 --- /dev/null +++ b/.circleci/unittest/linux/docker/Dockerfile @@ -0,0 +1,59 @@ +FROM ubuntu:18.04 as builder + +RUN apt update -q + +################################################################################ +# Build Kaldi +################################################################################ +RUN apt install -q -y \ + autoconf \ + automake \ + bzip2 \ + g++ \ + gfortran \ + git \ + libatlas-base-dev \ + libtool \ + make \ + python2.7 \ + python3 \ + sox \ + subversion \ + unzip \ + wget \ + zlib1g-dev + +# KALDI uses MKL as a default math library, but we are going to copy featbin binaries and dependent +# shared libraries to the final image, so we use ATLAS, which is easy to reinstall in the final image. +RUN git clone --depth 1 https://github.com/kaldi-asr/kaldi.git /opt/kaldi && \ + cd /opt/kaldi/tools && \ + make -j $(nproc) && \ + cd /opt/kaldi/src && \ + ./configure --shared --mathlib=ATLAS --use-cuda=no && \ + make featbin -j $(nproc) + +# Copy featbins and dependent libraries +ADD ./scripts /scripts +RUN bash /scripts/copy_kaldi_executables.sh /opt/kaldi /kaldi + +################################################################################ +# Build the final image +################################################################################ +FROM BASE_IMAGE +RUN apt update && apt install -y \ + g++ \ + gfortran \ + git \ + libatlas3-base \ + wget \ + curl \ + make \ + file \ + sox \ + libsox-dev \ + libsox-fmt-all \ + cmake \ + pkg-config \ + && rm -rf /var/lib/apt/lists/* +COPY --from=builder /kaldi /kaldi +ENV PATH="${PATH}:/kaldi/bin" LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:/kaldi/lib" diff --git a/.circleci/unittest/linux/docker/build_and_push.sh b/.circleci/unittest/linux/docker/build_and_push.sh new file mode 100755 index 0000000000..8bf33eaaa3 --- /dev/null +++ b/.circleci/unittest/linux/docker/build_and_push.sh @@ -0,0 +1,27 @@ +#!/usr/bin/env bash + +set -euo pipefail + +if [ $# -ne 1 ]; then + printf "Usage %s \n\n" "$0" + exit 1 +fi + +if [ "$1" = "cpu" ]; then + base_image="ubuntu:18.04" + image="pytorch/torchaudio_unittest_base:manylinux" +elif [[ "$1" =~ ^(9.2|10.1)$ ]]; then + base_image="nvidia/cuda:$1-runtime-ubuntu18.04" + image="pytorch/torchaudio_unittest_base:manylinux-cuda$1" +else + printf "Unexpected string: %s" "$1" + exit 1; +fi + +cd "$( dirname "${BASH_SOURCE[0]}" )" + +# docker build also accepts reading from STDIN +# but in that case, no context (other files) can be passed, so we write out Dockerfile +sed "s|BASE_IMAGE|${base_image}|g" Dockerfile > Dockerfile.tmp +docker build -t "${image}" -f Dockerfile.tmp . +docker push "${image}" diff --git a/.circleci/unittest/linux/docker/scripts/copy_kaldi_executables.sh b/.circleci/unittest/linux/docker/scripts/copy_kaldi_executables.sh new file mode 100755 index 0000000000..b0cf207143 --- /dev/null +++ b/.circleci/unittest/linux/docker/scripts/copy_kaldi_executables.sh @@ -0,0 +1,58 @@ +#!/usr/bin/env bash + +list_executables() { + # List up executables in the given directory + find "$1" -type f -executable +} + +list_kaldi_libraries() { + # List up shared libraries used by executables found in the given directory ($1) + # that reside in Kaldi directory ($2) + while read file; do + ldd "${file}" | grep -o "${2}.* "; + done < <(list_executables "$1") | sort -u +} + +set -euo pipefail + +kaldi_root="$(realpath "$1")" +target_dir="$(realpath "$2")" + +bin_dir="${target_dir}/bin" +lib_dir="${target_dir}/lib" + +mkdir -p "${bin_dir}" "${lib_dir}" + +# 1. Copy featbins +printf "Copying executables to %s\n" "${bin_dir}" +while read file; do + printf " %s\n" "${file}" + cp "${file}" "${bin_dir}" +done < <(list_executables "${kaldi_root}/src/featbin") + +# 2. Copy dependent libraries from Kaldi +printf "Copying libraries to %s\n" "${lib_dir}" +while read file; do + printf " %s\n" "$file" + # If it is not symlink, just copy to the target directory + if [ ! -L "${file}" ]; then + cp "${file}" "${lib_dir}" + continue + fi + + # If it is symlink, + # 1. Copy the actual library to the target directory. + library="$(realpath "${file}")" + cp "${library}" "${lib_dir}" + # 2. then if the name of the symlink is different from the actual library name, + # create the symlink in the target directory. + lib_name="$(basename "${library}")" + link_name="$(basename "${file}")" + if [ "${lib_name}" != "${link_name}" ]; then + printf " Linking %s -> %s\n" "${lib_name}" "${link_name}" + ( + cd "${lib_dir}" + ln -sf "${lib_name}" "${link_name}" + ) + fi +done < <(list_kaldi_libraries "${bin_dir}" "${kaldi_root}") diff --git a/.circleci/unittest/linux/scripts/environment.yml b/.circleci/unittest/linux/scripts/environment.yml new file mode 100644 index 0000000000..108d97d193 --- /dev/null +++ b/.circleci/unittest/linux/scripts/environment.yml @@ -0,0 +1,17 @@ +channels: + - conda-forge + - defaults +dependencies: + - flake8 + - numpy + - pytest + - pytest-cov + - codecov + - librosa + - llvmlite==0.31 # See https://github.com/pytorch/audio/pull/766 + - pip + - pip: + - kaldi-io + - scipy + - parameterized + - numba==0.48 # See https://github.com/librosa/librosa/issues/1160 diff --git a/.circleci/unittest/linux/scripts/install.sh b/.circleci/unittest/linux/scripts/install.sh new file mode 100755 index 0000000000..131c9e43bf --- /dev/null +++ b/.circleci/unittest/linux/scripts/install.sh @@ -0,0 +1,23 @@ +#!/usr/bin/env bash + +unset PYTORCH_VERSION +# For unittest, nightly PyTorch is used as the following section, +# so no need to set PYTORCH_VERSION. +# In fact, keeping PYTORCH_VERSION forces us to hardcode PyTorch version in config. + +set -e + +eval "$(./conda/bin/conda shell.bash hook)" +conda activate ./env + +if [ -z "${CUDA_VERSION:-}" ] ; then + cudatoolkit="cpuonly" +else + version="$(python -c "print('.'.join(\"${CUDA_VERSION}\".split('.')[:2]))")" + cudatoolkit="cudatoolkit=${version}" +fi +printf "Installing PyTorch with %s\n" "${cudatoolkit}" +conda install -y -c pytorch-nightly pytorch "${cudatoolkit}" + +printf "* Installing torchaudio\n" +BUILD_SOX=1 python setup.py develop diff --git a/.circleci/unittest/linux/scripts/post_process.sh b/.circleci/unittest/linux/scripts/post_process.sh new file mode 100755 index 0000000000..a84a0dea55 --- /dev/null +++ b/.circleci/unittest/linux/scripts/post_process.sh @@ -0,0 +1,8 @@ +#!/usr/bin/env bash + +set -e + +eval "$(./conda/bin/conda shell.bash hook)" +conda activate ./env + +codecov diff --git a/.circleci/unittest/linux/scripts/run_style_checks.sh b/.circleci/unittest/linux/scripts/run_style_checks.sh new file mode 100755 index 0000000000..70b83e19f5 --- /dev/null +++ b/.circleci/unittest/linux/scripts/run_style_checks.sh @@ -0,0 +1,32 @@ +#!/usr/bin/env bash + +set -u + +eval "$(./conda/bin/conda shell.bash hook)" +conda activate ./env + +# We want to run all the style checks even if one of them fail. + +exit_status=0 + +printf "\x1b[34mRunning flake8: " +flake8 --version +printf "\x1b[0m\n" +flake8 torchaudio test build_tools/setup_helpers +status=$? +exit_status="$((exit_status+status))" +if [ "${status}" -ne 0 ]; then + printf "\x1b[31mflake8 failed. Check the format of Python files.\x1b[0m\n" +fi + +printf "\x1b[34mRunning clang-format: " +clang-format --version +printf "\x1b[0m\n" +git-clang-format origin/master +git diff --exit-code +status=$? +exit_status="$((exit_status+status))" +if [ "${status}" -ne 0 ]; then + printf "\x1b[31mC++ files are not formatted. Please use git-clang-format to format CPP files.\x1b[0m\n" +fi +exit $exit_status diff --git a/.circleci/unittest/linux/scripts/run_test.sh b/.circleci/unittest/linux/scripts/run_test.sh new file mode 100755 index 0000000000..9ea942851e --- /dev/null +++ b/.circleci/unittest/linux/scripts/run_test.sh @@ -0,0 +1,10 @@ +#!/usr/bin/env bash + +set -e + +eval "$(./conda/bin/conda shell.bash hook)" +conda activate ./env + +python -m torch.utils.collect_env +export PATH="${PWD}/third_party/install/bin/:${PATH}" +pytest --cov=torchaudio --junitxml=test-results/junit.xml -v --durations 20 test diff --git a/.circleci/unittest/linux/scripts/setup_env.sh b/.circleci/unittest/linux/scripts/setup_env.sh new file mode 100755 index 0000000000..290f0f8f97 --- /dev/null +++ b/.circleci/unittest/linux/scripts/setup_env.sh @@ -0,0 +1,50 @@ +#!/usr/bin/env bash + +# This script is for setting up environment in which unit test is ran. +# To speed up the CI time, the resulting environment is cached. +# +# Do not install PyTorch and torchaudio here, otherwise they also get cached. + +set -e + +this_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" +root_dir="$(git rev-parse --show-toplevel)" +conda_dir="${root_dir}/conda" +env_dir="${root_dir}/env" + +cd "${root_dir}" + +case "$(uname -s)" in + Darwin*) os=MacOSX;; + *) os=Linux +esac + +# 1. Install conda at ./conda +if [ ! -d "${conda_dir}" ]; then + printf "* Installing conda\n" + wget -O miniconda.sh "http://repo.continuum.io/miniconda/Miniconda3-latest-${os}-x86_64.sh" + bash ./miniconda.sh -b -f -p "${conda_dir}" +fi +eval "$(${conda_dir}/bin/conda shell.bash hook)" + +# 2. Create test environment at ./env +if [ ! -d "${env_dir}" ]; then + printf "* Creating a test environment\n" + conda create --prefix "${env_dir}" -y python="$PYTHON_VERSION" +fi +conda activate "${env_dir}" + +# 3. Install Conda dependencies +printf "* Installing dependencies (except PyTorch)\n" +conda env update --file "${this_dir}/environment.yml" --prune +if [ "${os}" == Linux ] ; then + pip install clang-format +fi + +# 4. Buld codecs +mkdir -p third_party/build +( + cd third_party/build + cmake .. + cmake --build . +) diff --git a/.circleci/unittest/windows/README.md b/.circleci/unittest/windows/README.md new file mode 100644 index 0000000000..2c06af62bd --- /dev/null +++ b/.circleci/unittest/windows/README.md @@ -0,0 +1,4 @@ +This directory contains; + + - scripts + Scripts used by CircleCI to run unit tests. diff --git a/.circleci/unittest/windows/scripts/environment.yml b/.circleci/unittest/windows/scripts/environment.yml new file mode 100644 index 0000000000..fb57f0facc --- /dev/null +++ b/.circleci/unittest/windows/scripts/environment.yml @@ -0,0 +1,18 @@ +channels: + - conda-forge + - defaults +dependencies: + - flake8 + - numpy + - pytest + - pytest-cov + - codecov + - pip + - pip: + - scipy == 1.4.1 + - kaldi-io + - PySoundFile + - librosa + - future + - parameterized + - numba==0.48 diff --git a/.circleci/unittest/windows/scripts/install.sh b/.circleci/unittest/windows/scripts/install.sh new file mode 100644 index 0000000000..acbd79ae50 --- /dev/null +++ b/.circleci/unittest/windows/scripts/install.sh @@ -0,0 +1,23 @@ +#!/usr/bin/env bash + +unset PYTORCH_VERSION +# For unittest, nightly PyTorch is used as the following section, +# so no need to set PYTORCH_VERSION. +# In fact, keeping PYTORCH_VERSION forces us to hardcode PyTorch version in config. + +set -e + +eval "$(./conda/Scripts/conda.exe 'shell.bash' 'hook')" +conda activate ./env + +if [ -z "${CUDA_VERSION:-}" ] ; then + cudatoolkit="cpuonly" +else + version="$(python -c "print('.'.join(\"${CUDA_VERSION}\".split('.')[:2]))")" + cudatoolkit="cudatoolkit=${version}" +fi +printf "Installing PyTorch with %s\n" "${cudatoolkit}" +conda install -y -c pytorch-nightly pytorch "${cudatoolkit}" + +printf "* Installing torchaudio\n" +python setup.py develop diff --git a/.circleci/unittest/windows/scripts/install_conda.bat b/.circleci/unittest/windows/scripts/install_conda.bat new file mode 100644 index 0000000000..6052ad08b1 --- /dev/null +++ b/.circleci/unittest/windows/scripts/install_conda.bat @@ -0,0 +1 @@ +start /wait "" "%miniconda_exe%" /S /InstallationType=JustMe /RegisterPython=0 /AddToPath=0 /D=%tmp_conda% diff --git a/.circleci/unittest/windows/scripts/post_process.sh b/.circleci/unittest/windows/scripts/post_process.sh new file mode 100644 index 0000000000..b132113194 --- /dev/null +++ b/.circleci/unittest/windows/scripts/post_process.sh @@ -0,0 +1,8 @@ +#!/usr/bin/env bash + +set -e + +eval "$(./conda/Scripts/conda.exe 'shell.bash' 'hook')" +conda activate ./env + +codecov diff --git a/.circleci/unittest/windows/scripts/run_test.sh b/.circleci/unittest/windows/scripts/run_test.sh new file mode 100644 index 0000000000..252c1e9f46 --- /dev/null +++ b/.circleci/unittest/windows/scripts/run_test.sh @@ -0,0 +1,10 @@ +#!/usr/bin/env bash + +set -e + +eval "$(./conda/Scripts/conda.exe 'shell.bash' 'hook')" +conda activate ./env + +python -m torch.utils.collect_env +pytest --cov=torchaudio --junitxml=test-results/junit.xml -v --durations 20 test +flake8 torchaudio test diff --git a/.circleci/unittest/windows/scripts/setup_env.sh b/.circleci/unittest/windows/scripts/setup_env.sh new file mode 100644 index 0000000000..a45cf856ac --- /dev/null +++ b/.circleci/unittest/windows/scripts/setup_env.sh @@ -0,0 +1,39 @@ +#!/usr/bin/env bash + +# This script is for setting up environment in which unit test is ran. +# To speed up the CI time, the resulting environment is cached. +# +# Do not install PyTorch and torchaudio here, otherwise they also get cached. + +set -e + +this_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" +root_dir="$(git rev-parse --show-toplevel)" +conda_dir="${root_dir}/conda" +env_dir="${root_dir}/env" + +cd "${root_dir}" + +# 1. Install conda at ./conda +if [ ! -d "${conda_dir}" ]; then + printf "* Installing conda\n" + export tmp_conda="$(echo $conda_dir | tr '/' '\\')" + export miniconda_exe="$(echo $root_dir | tr '/' '\\')\\miniconda.exe" + curl --output miniconda.exe https://repo.anaconda.com/miniconda/Miniconda3-latest-Windows-x86_64.exe -O + "$this_dir/install_conda.bat" + unset tmp_conda + unset miniconda_exe +fi + +eval "$(${conda_dir}/Scripts/conda.exe 'shell.bash' 'hook')" + +# 2. Create test environment at ./env +if [ ! -d "${env_dir}" ]; then + printf "* Creating a test environment\n" + conda create --prefix "${env_dir}" -y python="$PYTHON_VERSION" +fi +conda activate "${env_dir}" + +# 3. Install Conda dependencies +printf "* Installing dependencies (except PyTorch)\n" +conda env update --file "${this_dir}/environment.yml" --prune diff --git a/.clang-format b/.clang-format new file mode 100644 index 0000000000..73304266bd --- /dev/null +++ b/.clang-format @@ -0,0 +1,88 @@ +--- +AccessModifierOffset: -1 +AlignAfterOpenBracket: AlwaysBreak +AlignConsecutiveAssignments: false +AlignConsecutiveDeclarations: false +AlignEscapedNewlinesLeft: true +AlignOperands: false +AlignTrailingComments: false +AllowAllParametersOfDeclarationOnNextLine: false +AllowShortBlocksOnASingleLine: false +AllowShortCaseLabelsOnASingleLine: false +AllowShortFunctionsOnASingleLine: Empty +AllowShortIfStatementsOnASingleLine: false +AllowShortLoopsOnASingleLine: false +AlwaysBreakAfterReturnType: None +AlwaysBreakBeforeMultilineStrings: true +AlwaysBreakTemplateDeclarations: true +BinPackArguments: false +BinPackParameters: false +BraceWrapping: + AfterClass: false + AfterControlStatement: false + AfterEnum: false + AfterFunction: false + AfterNamespace: false + AfterObjCDeclaration: false + AfterStruct: false + AfterUnion: false + BeforeCatch: false + BeforeElse: false + IndentBraces: false +BreakBeforeBinaryOperators: None +BreakBeforeBraces: Attach +BreakBeforeTernaryOperators: true +BreakConstructorInitializersBeforeComma: false +BreakAfterJavaFieldAnnotations: false +BreakStringLiterals: false +ColumnLimit: 80 +CommentPragmas: '^ IWYU pragma:' +CompactNamespaces: false +ConstructorInitializerAllOnOneLineOrOnePerLine: true +ConstructorInitializerIndentWidth: 4 +ContinuationIndentWidth: 4 +Cpp11BracedListStyle: true +DerivePointerAlignment: false +DisableFormat: false +ForEachMacros: [ FOR_EACH_RANGE, FOR_EACH, ] +IncludeCategories: + - Regex: '^<.*\.h(pp)?>' + Priority: 1 + - Regex: '^<.*' + Priority: 2 + - Regex: '.*' + Priority: 3 +IndentCaseLabels: true +IndentWidth: 2 +IndentWrappedFunctionNames: false +KeepEmptyLinesAtTheStartOfBlocks: false +MacroBlockBegin: '' +MacroBlockEnd: '' +MaxEmptyLinesToKeep: 1 +NamespaceIndentation: None +ObjCBlockIndentWidth: 2 +ObjCSpaceAfterProperty: false +ObjCSpaceBeforeProtocolList: false +PenaltyBreakBeforeFirstCallParameter: 1 +PenaltyBreakComment: 300 +PenaltyBreakFirstLessLess: 120 +PenaltyBreakString: 1000 +PenaltyExcessCharacter: 1000000 +PenaltyReturnTypeOnItsOwnLine: 2000000 +PointerAlignment: Left +ReflowComments: true +SortIncludes: true +SpaceAfterCStyleCast: false +SpaceBeforeAssignmentOperators: true +SpaceBeforeParens: ControlStatements +SpaceInEmptyParentheses: false +SpacesBeforeTrailingComments: 1 +SpacesInAngles: false +SpacesInContainerLiterals: true +SpacesInCStyleCastParentheses: false +SpacesInParentheses: false +SpacesInSquareBrackets: false +Standard: Cpp11 +TabWidth: 8 +UseTab: Never +... diff --git a/.clang-tidy b/.clang-tidy new file mode 100644 index 0000000000..a3a40b5907 --- /dev/null +++ b/.clang-tidy @@ -0,0 +1,33 @@ +--- +# NOTE there must be no spaces before the '-', so put the comma first. +Checks: ' + -* + ,bugprone-* + ,-bugprone-forward-declaration-namespace + ,-bugprone-macro-parentheses + ,cppcoreguidelines-* + ,-cppcoreguidelines-interfaces-global-init + ,-cppcoreguidelines-owning-memory + ,-cppcoreguidelines-pro-bounds-array-to-pointer-decay + ,-cppcoreguidelines-pro-bounds-constant-array-index + ,-cppcoreguidelines-pro-bounds-pointer-arithmetic + ,-cppcoreguidelines-pro-type-cstyle-cast + ,-cppcoreguidelines-pro-type-reinterpret-cast + ,-cppcoreguidelines-pro-type-static-cast-downcast + ,-cppcoreguidelines-pro-type-union-access + ,-cppcoreguidelines-pro-type-vararg + ,-cppcoreguidelines-special-member-functions + ,hicpp-exception-baseclass + ,hicpp-avoid-goto + ,modernize-* + ,-modernize-return-braced-init-list + ,-modernize-use-auto + ,-modernize-use-default-member-init + ,-modernize-use-using + ,performance-unnecessary-value-param + ' +WarningsAsErrors: '*' +HeaderFilterRegex: 'torchaudio/.*' +AnalyzeTemporaryDtors: false +CheckOptions: +... diff --git a/.flake8 b/.flake8 new file mode 100644 index 0000000000..9d0eaed2bd --- /dev/null +++ b/.flake8 @@ -0,0 +1,4 @@ +[flake8] +max-line-length = 120 +ignore = E305,E402,E721,E741,F401,F403,F405,F999,W503,W504 +exclude = build,docs/source,_ext,third_party diff --git a/.github/ISSUE_TEMPLATE/bug-report.md b/.github/ISSUE_TEMPLATE/bug-report.md new file mode 100644 index 0000000000..fb50bd8504 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/bug-report.md @@ -0,0 +1,53 @@ +--- +name: "\U0001F41B Bug Report" +about: Submit a bug report to help us improve Torchaudio + +--- + +## ๐Ÿ› Bug + + + +## To Reproduce + +Steps to reproduce the behavior: + +1. +1. +1. + + + +## Expected behavior + + + +## Environment + + - What commands did you used to install torchaudio (conda/pip/build from source)? + - If you are building from source, which commit is it? + - What does `torchaudio.__version__` print? (If applicable) + +Please copy and paste the output from our +[environment collection script](https://raw.githubusercontent.com/pytorch/pytorch/master/torch/utils/collect_env.py) +(or fill out the checklist below manually). + +You can get the script and run it with: +``` +wget https://raw.githubusercontent.com/pytorch/pytorch/master/torch/utils/collect_env.py +# For security purposes, please check the contents of collect_env.py before running it. +python collect_env.py +``` + + - PyTorch Version (e.g., 1.0): + - OS (e.g., Linux): + - How you installed PyTorch (`conda`, `pip`, source): + - Build command you used (if compiling from source): + - Python version: + - CUDA/cuDNN version: + - GPU models and configuration: + - Any other relevant information: + +## Additional context + + diff --git a/.github/ISSUE_TEMPLATE/documentation.md b/.github/ISSUE_TEMPLATE/documentation.md new file mode 100644 index 0000000000..ae745121c5 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/documentation.md @@ -0,0 +1,9 @@ +--- +name: "\U0001F4DA Documentation" +about: Report an issue related to https://pytorch.org/audio + +--- + +## ๐Ÿ“š Documentation + + diff --git a/.github/ISSUE_TEMPLATE/feature-request.md b/.github/ISSUE_TEMPLATE/feature-request.md new file mode 100644 index 0000000000..f3896e5368 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/feature-request.md @@ -0,0 +1,24 @@ +--- +name: "\U0001F680Feature Request" +about: Submit a proposal/request for a new Torchaudio feature + +--- + +## ๐Ÿš€ Feature + + +## Motivation + + + +## Pitch + + + +## Alternatives + + + +## Additional context + + diff --git a/.github/ISSUE_TEMPLATE/questions-help-support.md b/.github/ISSUE_TEMPLATE/questions-help-support.md new file mode 100644 index 0000000000..77bfb55b9a --- /dev/null +++ b/.github/ISSUE_TEMPLATE/questions-help-support.md @@ -0,0 +1,13 @@ +--- +name: "โ“Questions/Help/Support" +about: Do you need support? We have resources. + +--- + +## โ“ Questions and Help + +### Please note that this issue tracker is not a help form and this issue will be closed. + +We have a set of [listed resources available on the website](https://pytorch.org/resources). Our primary means of support is our discussion forum: + +- [Discussion Forum](https://discuss.pytorch.org/) diff --git a/.github/pytorch-probot.yml b/.github/pytorch-probot.yml new file mode 100644 index 0000000000..d9daca0fb2 --- /dev/null +++ b/.github/pytorch-probot.yml @@ -0,0 +1 @@ +tracking_issue: 736 diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000..e8a9d4c409 --- /dev/null +++ b/.gitignore @@ -0,0 +1,129 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# temp files +~* +*.swp + +# C extensions / folders +*.so +_ext/ + +# Distribution / packaging +.Python +env/ +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +*.egg-info/ +.installed.cfg +*.egg + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +.hypothesis/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ +docs/src/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints/ + +# pyenv +.python-version + +# celery beat schedule file +celerybeat-schedule + +# SageMath parsed files +*.sage.py + +# dotenv +.env + +# virtualenv +.venv +venv/ +ENV/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# PyCharm project settings +.idea + +# OSX dir files +.DS_Store + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ + +# Generated Files +test/assets/sinewave.wav +torchaudio/version.py +gen.yml + +# Examples +examples/interactive_asr/data/*.txt +examples/interactive_asr/data/*.model +examples/interactive_asr/data/*.pt + +# third parties +third_party/archives/ +third_party/install/ +third_party/src/ +third_party/tmp/ diff --git a/.travis.yml b/.travis.yml new file mode 100644 index 0000000000..81e37b4120 --- /dev/null +++ b/.travis.yml @@ -0,0 +1,32 @@ +# note dist: 'trusty' does not work here +dist: xenial + +language: python + +# cache miniconda installer and similar files +cache: + directories: + - /home/travis/download + +# This matrix tests that the code works on Python 3.5, 3.6, 3.7, passes +# lint and example tests. +matrix: + fast_finish: true + include: + - env: PYTHON_VERSION="3.6" RUN_EXAMPLE_TESTS="true" SKIP_TESTS="true" + allow_failures: + - env: PYTHON_VERSION="3.6" RUN_EXAMPLE_TESTS="true" SKIP_TESTS="true" + +addons: + apt: + packages: + sox + libsox-dev + libsox-fmt-all + portaudio19-dev + +notifications: + email: false + +install: source build_tools/travis/install.sh +script: bash build_tools/travis/test_script.sh diff --git a/docs/source/backend.rst b/docs/source/backend.rst new file mode 100644 index 0000000000..6b0e074d5c --- /dev/null +++ b/docs/source/backend.rst @@ -0,0 +1,138 @@ +.. _backend: + +torchaudio.backend +================== + +:mod:`torchaudio.backend` module provides implementations for audio file I/O, using different backend libraries. +To switch backend, use :py:func:`torchaudio.set_audio_backend`. To check the current backend use :py:func:`torchaudio.get_audio_backend`. + +.. warning:: + Although ``sox`` backend is default for backward compatibility reason, it has a number of issues, therefore it is highly recommended to use ``sox_io`` backend instead. Note, however, that due to the interface refinement, functions defined in ``sox`` backend and those defined in ``sox_io`` backend do not have the same signatures. + +.. note:: + Instead of calling functions in :mod:`torchaudio.backend` directly, please use ``torchaudio.info``, ``torhcaudio.load``, ``torchaudio.load_wav`` and ``torchaudio.save`` with proper backend set with :func:`torchaudio.get_audio_backend`. + +There are currently three implementations available. + + * :ref:`sox` + * :ref:`sox_io` + * :ref:`soundfile` + +``sox`` backend is the original backend which is built on ``libsox``. This module is currently default but is known to have number of issues, such as wrong handling of WAV files other than 16-bit signed integer. Users are encouraged to use ``sox_io`` backend. This backend requires C++ extension module and is not available on Windows system. + +``sox_io`` backend is the new backend which is built on ``libsox`` and bound to Python with ``Torchscript``. This module addresses all the known issues ``sox`` backend has. Function calls to this backend can be Torchscriptable. This backend requires C++ extension module and is not available on Windows system. + +``soundfile`` backend is built on ``PySoundFile``. You need to install ``PySoundFile`` separately. + +Common Data Structure +~~~~~~~~~~~~~~~~~~~~~ + +Structures used to exchange data between Python interface and ``libsox``. They are used by :ref:`sox` and :ref:`soundfile` but not by :ref:`sox_io`. + +.. autoclass:: torchaudio.backend.common.SignalInfo + +.. autoclass:: torchaudio.backend.common.EncodingInfo + +.. _sox_backend: + +Sox Backend +~~~~~~~~~~~ + +``sox`` backend is available on ``torchaudio`` installation with C++ extension. It is currently not available on Windows system. + +It is currently default backend when it's available. You can switch from another backend to ``sox`` backend with the following; + +.. code:: + + torchaudio.set_audio_backend("sox") + +info +---- + +.. autofunction:: torchaudio.backend.sox_backend.info + +load +---- + +.. autofunction:: torchaudio.backend.sox_backend.load + +.. autofunction:: torchaudio.backend.sox_backend.load_wav + + +save +---- + +.. autofunction:: torchaudio.backend.sox_backend.save + +others +------ + +.. automodule:: torchaudio.backend.sox_backend + :members: + :exclude-members: info, load, load_wav, save + +.. _sox_io_backend: + +Sox IO Backend +~~~~~~~~~~~~~~ + +``sox_io`` backend is available on ``torchaudio`` installation with C++ extension. It is currently not available on Windows system. + +This new backend is recommended over ``sox`` backend. You can switch from another backend to ``sox_io`` backend with the following; + +.. code:: + + torchaudio.set_audio_backend("sox_io") + +The function call to this backend can be Torchsript-able. You can apply :func:`torch.jit.script` and dump the object to file, then call it from C++ application. + +info +---- + +.. autoclass:: torchaudio.backend.sox_io_backend.AudioMetaData + +.. autofunction:: torchaudio.backend.sox_io_backend.info + +load +---- + +.. autofunction:: torchaudio.backend.sox_io_backend.load + +.. autofunction:: torchaudio.backend.sox_io_backend.load_wav + + +save +---- + +.. autofunction:: torchaudio.backend.sox_io_backend.save + +.. _soundfile_backend: + +Soundfile Backend +~~~~~~~~~~~~~~~~~ + +``soundfile`` backend is available when ``PySoundFile`` is installed. This backend works on ``torchaudio`` installation without C++ extension. (i.e. Windows) + +You can switch from another backend to ``soundfile`` backend with the following; + +.. code:: + + torchaudio.set_audio_backend("soundfile") + +info +---- + +.. autofunction:: torchaudio.backend.soundfile_backend.info + +load +---- + +.. autofunction:: torchaudio.backend.soundfile_backend.load + +.. autofunction:: torchaudio.backend.soundfile_backend.load_wav + + +save +---- + +.. autofunction:: torchaudio.backend.soundfile_backend.save diff --git a/docs/source/datasets.rst b/docs/source/datasets.rst index 2f844293f6..96ba045c5e 100644 --- a/docs/source/datasets.rst +++ b/docs/source/datasets.rst @@ -57,6 +57,14 @@ LIBRISPEECH :special-members: +LIBRITTS +~~~~~~~~ + +.. autoclass:: LIBRITTS + :members: __getitem__ + :special-members: + + LJSPEECH ~~~~~~~~ diff --git a/docs/source/index.rst b/docs/source/index.rst index c6d0efde69..8146e545c3 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -1,5 +1,5 @@ torchaudio -=========== +========== The :mod:`torchaudio` package consists of I/O, popular datasets and common audio transformations. @@ -7,12 +7,13 @@ The :mod:`torchaudio` package consists of I/O, popular datasets and common audio :maxdepth: 2 :caption: Package Reference - sox_effects + torchaudio + backend + functional + transforms datasets + models + sox_effects compliance.kaldi kaldi_io - transforms - functional - -.. automodule:: torchaudio - :members: + utils diff --git a/docs/source/sox_effects.rst b/docs/source/sox_effects.rst index 56cd985d0a..0aa34e7908 100644 --- a/docs/source/sox_effects.rst +++ b/docs/source/sox_effects.rst @@ -1,21 +1,52 @@ -.. role:: hidden - :class: hidden-section +.. _sox_effects: torchaudio.sox_effects ====================== -Create SoX effects chain for preprocessing audio. - .. currentmodule:: torchaudio.sox_effects -:hidden:`SoxEffect` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +.. warning:: + + The :py:class:`SoxEffect` and :py:class:`SoxEffectsChain` classes are deprecated. Please migrate to :func:`apply_effects_tensor` and :func:`apply_effects_file`. + +Resource initialization / shutdown +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autofunction:: init_sox_effects + +.. autofunction:: shutdown_sox_effects + +Listing supported effects +~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autofunction:: effect_names + +Applying effects +~~~~~~~~~~~~~~~~ + +Apply SoX effects chain on torch.Tensor or on file and load as torch.Tensor. + +Applying effects on Tensor +-------------------------- + +.. autofunction:: apply_effects_tensor + +Applying effects on file +------------------------ + +.. autofunction:: apply_effects_file + +Legacy +~~~~~~ + +SoxEffect +--------- .. autoclass:: SoxEffect :members: -:hidden:`SoxEffectsChain` -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +SoxEffectsChain +--------------- .. autoclass:: SoxEffectsChain :members: append_effect_to_chain, sox_build_flow_effects, clear_chain, set_input_file diff --git a/docs/source/torchaudio.rst b/docs/source/torchaudio.rst new file mode 100644 index 0000000000..f7b826b571 --- /dev/null +++ b/docs/source/torchaudio.rst @@ -0,0 +1,43 @@ +torchaudio +========== + +I/O functionalities +~~~~~~~~~~~~~~~~~~~ + +Audio I/O functions are implemented in :ref:`torchaudio.backend` module, but for the ease of use, the following functions are made available on :mod:`torchaudio` module. There are different backends available and you can switch backends with :func:`set_audio_backend`. + +Refer to :ref:`backend` for the detail. + +.. function:: torchaudio.info(filepath: str, ...) + + Fetch meta data of an audio file. Refer to :ref:`backend` for the detail. + +.. function:: torchaudio.load(filepath: str, ...) + + Load audio file into torch.Tensor object. Refer to :ref:`backend` for the detail. + +.. function:: torchaudio.load_wav(filepath: str, ...) + + Load audio file into torch.Tensor, Refer to :ref:`backend` for the detail. + +.. function:: torchaudio.save(filepath: str, src: torch.Tensor, sample_rate: int, ...) + + Save torch.Tensor object into an audio format. Refer to :ref:`backend` for the detail. + +.. currentmodule:: torchaudio + +Backend Utilities +~~~~~~~~~~~~~~~~~ + +.. autofunction:: list_audio_backends + +.. autofunction:: get_audio_backend + +.. autofunction:: set_audio_backend + +Sox Effects Utilities +~~~~~~~~~~~~~~~~~~~~~ + +.. autofunction:: initialize_sox + +.. autofunction:: shutdown_sox diff --git a/docs/source/utils.rst b/docs/source/utils.rst new file mode 100644 index 0000000000..dc5ad0fd73 --- /dev/null +++ b/docs/source/utils.rst @@ -0,0 +1,11 @@ +torchaudio.utils +================ + +torchaudio.utils.sox_utils +~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Utility module to configure libsox. +This affects functionalities in :ref:`Sox IO backend` and :ref:`Sox Effects`. + +.. automodule:: torchaudio.utils.sox_utils + :members: diff --git a/examples/pipeline_wavernn/README.md b/examples/pipeline_wavernn/README.md new file mode 100644 index 0000000000..865f06c181 --- /dev/null +++ b/examples/pipeline_wavernn/README.md @@ -0,0 +1,36 @@ +This is an example vocoder pipeline using the WaveRNN model trained with LJSpeech. WaveRNN model is based on the implementation from [this repository](https://github.com/fatchord/WaveRNN). The original implementation was +introduced in "Efficient Neural Audio Synthesis". WaveRNN and LJSpeech are available in torchaudio. + +### Usage + +An example can be invoked as follows. +``` +python main.py \ + --batch-size 256 \ + --learning-rate 1e-4 \ + --n-freq 80 \ + --loss 'crossentropy' \ + --n-bits 8 \ +``` + +### Output + +The information reported at each iteration and epoch (e.g. loss) is printed to standard output in the form of one json per line. Here is an example python function to parse the output if redirected to a file. +```python +def read_json(filename): + """ + Convert the standard output saved to filename into a pandas dataframe for analysis. + """ + + import pandas + import json + + with open(filename, "r") as f: + data = f.read() + + # pandas doesn't read single quotes for json + data = data.replace("'", '"') + + data = [json.loads(l) for l in data.splitlines()] + return pandas.DataFrame(data) +``` diff --git a/examples/pipeline_wavernn/datasets.py b/examples/pipeline_wavernn/datasets.py new file mode 100644 index 0000000000..8d3068a229 --- /dev/null +++ b/examples/pipeline_wavernn/datasets.py @@ -0,0 +1,115 @@ +import os +import random + +import torch +import torchaudio +from torch.utils.data.dataset import random_split +from torchaudio.datasets import LJSPEECH +from torchaudio.transforms import MuLawEncoding + +from processing import bits_to_normalized_waveform, normalized_waveform_to_bits + + +class MapMemoryCache(torch.utils.data.Dataset): + r"""Wrap a dataset so that, whenever a new item is returned, it is saved to memory. + """ + + def __init__(self, dataset): + self.dataset = dataset + self._cache = [None] * len(dataset) + + def __getitem__(self, n): + if self._cache[n] is not None: + return self._cache[n] + + item = self.dataset[n] + self._cache[n] = item + + return item + + def __len__(self): + return len(self.dataset) + + +class Processed(torch.utils.data.Dataset): + def __init__(self, dataset, transforms): + self.dataset = dataset + self.transforms = transforms + + def __getitem__(self, key): + item = self.dataset[key] + return self.process_datapoint(item) + + def __len__(self): + return len(self.dataset) + + def process_datapoint(self, item): + specgram = self.transforms(item[0]) + return item[0].squeeze(0), specgram + + +def split_process_ljspeech(args, transforms): + data = LJSPEECH(root=args.file_path, download=False) + + val_length = int(len(data) * args.val_ratio) + lengths = [len(data) - val_length, val_length] + train_dataset, val_dataset = random_split(data, lengths) + + train_dataset = Processed(train_dataset, transforms) + val_dataset = Processed(val_dataset, transforms) + + train_dataset = MapMemoryCache(train_dataset) + val_dataset = MapMemoryCache(val_dataset) + + return train_dataset, val_dataset + + +def collate_factory(args): + def raw_collate(batch): + + pad = (args.kernel_size - 1) // 2 + + # input waveform length + wave_length = args.hop_length * args.seq_len_factor + # input spectrogram length + spec_length = args.seq_len_factor + pad * 2 + + # max start postion in spectrogram + max_offsets = [x[1].shape[-1] - (spec_length + pad * 2) for x in batch] + + # random start postion in spectrogram + spec_offsets = [random.randint(0, offset) for offset in max_offsets] + # random start postion in waveform + wave_offsets = [(offset + pad) * args.hop_length for offset in spec_offsets] + + waveform_combine = [ + x[0][wave_offsets[i]: wave_offsets[i] + wave_length + 1] + for i, x in enumerate(batch) + ] + specgram = [ + x[1][:, spec_offsets[i]: spec_offsets[i] + spec_length] + for i, x in enumerate(batch) + ] + + specgram = torch.stack(specgram) + waveform_combine = torch.stack(waveform_combine) + + waveform = waveform_combine[:, :wave_length] + target = waveform_combine[:, 1:] + + # waveform: [-1, 1], target: [0, 2**bits-1] if loss = 'crossentropy' + if args.loss == "crossentropy": + + if args.mulaw: + mulaw_encode = MuLawEncoding(2 ** args.n_bits) + waveform = mulaw_encode(waveform) + target = mulaw_encode(target) + + waveform = bits_to_normalized_waveform(waveform, args.n_bits) + + else: + target = normalized_waveform_to_bits(target, args.n_bits) + + return waveform.unsqueeze(1), specgram.unsqueeze(1), target.unsqueeze(1) + + return raw_collate diff --git a/examples/pipeline_wavernn/losses.py b/examples/pipeline_wavernn/losses.py new file mode 100644 index 0000000000..a4494b05fb --- /dev/null +++ b/examples/pipeline_wavernn/losses.py @@ -0,0 +1,119 @@ +import math + +import torch +from torch import nn as nn +from torch.nn import functional as F + + +class LongCrossEntropyLoss(nn.Module): + r""" CrossEntropy loss + """ + + def __init__(self): + super(LongCrossEntropyLoss, self).__init__() + + def forward(self, output, target): + output = output.transpose(1, 2) + target = target.long() + + criterion = nn.CrossEntropyLoss() + return criterion(output, target) + + +class MoLLoss(nn.Module): + r""" Discretized mixture of logistic distributions loss + + Adapted from wavenet vocoder + (https://github.com/r9y9/wavenet_vocoder/blob/master/wavenet_vocoder/mixture.py) + Explanation of loss (https://github.com/Rayhane-mamah/Tacotron-2/issues/155) + + Args: + y_hat (Tensor): Predicted output (n_batch x n_time x n_channel) + y (Tensor): Target (n_batch x n_time x 1) + num_classes (int): Number of classes + log_scale_min (float): Log scale minimum value + reduce (bool): If True, the losses are averaged or summed for each minibatch + + Returns + Tensor: loss + """ + + def __init__(self, num_classes=65536, log_scale_min=None, reduce=True): + super(MoLLoss, self).__init__() + self.num_classes = num_classes + self.log_scale_min = log_scale_min + self.reduce = reduce + + def forward(self, y_hat, y): + y = y.unsqueeze(-1) + + if self.log_scale_min is None: + self.log_scale_min = math.log(1e-14) + + assert y_hat.dim() == 3 + assert y_hat.size(-1) % 3 == 0 + + nr_mix = y_hat.size(-1) // 3 + + # unpack parameters (n_batch, n_time, num_mixtures) x 3 + logit_probs = y_hat[:, :, :nr_mix] + means = y_hat[:, :, nr_mix: 2 * nr_mix] + log_scales = torch.clamp( + y_hat[:, :, 2 * nr_mix: 3 * nr_mix], min=self.log_scale_min + ) + + # (n_batch x n_time x 1) to (n_batch x n_time x num_mixtures) + y = y.expand_as(means) + + centered_y = y - means + inv_stdv = torch.exp(-log_scales) + plus_in = inv_stdv * (centered_y + 1.0 / (self.num_classes - 1)) + cdf_plus = torch.sigmoid(plus_in) + min_in = inv_stdv * (centered_y - 1.0 / (self.num_classes - 1)) + cdf_min = torch.sigmoid(min_in) + + # log probability for edge case of 0 (before scaling) + # equivalent: torch.log(F.sigmoid(plus_in)) + log_cdf_plus = plus_in - F.softplus(plus_in) + + # log probability for edge case of 255 (before scaling) + # equivalent: (1 - F.sigmoid(min_in)).log() + log_one_minus_cdf_min = -F.softplus(min_in) + + # probability for all other cases + cdf_delta = cdf_plus - cdf_min + + mid_in = inv_stdv * centered_y + # log probability in the center of the bin, to be used in extreme cases + log_pdf_mid = mid_in - log_scales - 2.0 * F.softplus(mid_in) + + inner_inner_cond = (cdf_delta > 1e-5).float() + + inner_inner_out = inner_inner_cond * torch.log( + torch.clamp(cdf_delta, min=1e-12) + ) + (1.0 - inner_inner_cond) * ( + log_pdf_mid - math.log((self.num_classes - 1) / 2) + ) + inner_cond = (y > 0.999).float() + inner_out = ( + inner_cond * log_one_minus_cdf_min + (1.0 - inner_cond) * inner_inner_out + ) + cond = (y < -0.999).float() + log_probs = cond * log_cdf_plus + (1.0 - cond) * inner_out + + log_probs = log_probs + F.log_softmax(logit_probs, -1) + + if self.reduce: + return -torch.mean(_log_sum_exp(log_probs)) + else: + return -_log_sum_exp(log_probs).unsqueeze(-1) + + +def _log_sum_exp(x): + r""" Numerically stable log_sum_exp implementation that prevents overflow + """ + + axis = len(x.size()) - 1 + m, _ = torch.max(x, dim=axis) + m2, _ = torch.max(x, dim=axis, keepdim=True) + return m + torch.log(torch.sum(torch.exp(x - m2), dim=axis)) diff --git a/examples/pipeline_wavernn/main.py b/examples/pipeline_wavernn/main.py new file mode 100644 index 0000000000..032e9fc70e --- /dev/null +++ b/examples/pipeline_wavernn/main.py @@ -0,0 +1,390 @@ +import argparse +import logging +import os +import signal +from collections import defaultdict +from datetime import datetime +from time import time +from typing import List + +import torch +import torchaudio +from torch import nn as nn +from torch.optim import Adam +from torch.utils.data import DataLoader +from torchaudio.datasets.utils import bg_iterator +from torchaudio.models._wavernn import _WaveRNN + +from datasets import collate_factory, split_process_ljspeech +from losses import LongCrossEntropyLoss, MoLLoss +from processing import LinearToMel, NormalizeDB +from utils import MetricLogger, count_parameters, save_checkpoint + + +def parse_args(): + parser = argparse.ArgumentParser() + + parser.add_argument( + "--workers", + default=4, + type=int, + metavar="N", + help="number of data loading workers", + ) + parser.add_argument( + "--checkpoint", + default="", + type=str, + metavar="PATH", + help="path to latest checkpoint", + ) + parser.add_argument( + "--epochs", + default=8000, + type=int, + metavar="N", + help="number of total epochs to run", + ) + parser.add_argument( + "--start-epoch", default=0, type=int, metavar="N", help="manual epoch number" + ) + parser.add_argument( + "--print-freq", + default=10, + type=int, + metavar="N", + help="print frequency in epochs", + ) + parser.add_argument( + "--batch-size", default=256, type=int, metavar="N", help="mini-batch size" + ) + parser.add_argument( + "--learning-rate", default=1e-4, type=float, metavar="LR", help="learning rate", + ) + parser.add_argument("--clip-grad", metavar="NORM", type=float, default=4.0) + parser.add_argument( + "--mulaw", + default=True, + action="store_true", + help="if used, waveform is mulaw encoded", + ) + parser.add_argument( + "--jit", default=False, action="store_true", help="if used, model is jitted" + ) + parser.add_argument( + "--upsample-scales", + default=[5, 5, 11], + type=List[int], + help="the list of upsample scales", + ) + parser.add_argument( + "--n-bits", default=8, type=int, help="the bits of output waveform", + ) + parser.add_argument( + "--sample-rate", + default=22050, + type=int, + help="the rate of audio dimensions (samples per second)", + ) + parser.add_argument( + "--hop-length", + default=275, + type=int, + help="the number of samples between the starts of consecutive frames", + ) + parser.add_argument( + "--win-length", default=1100, type=int, help="the length of the STFT window", + ) + parser.add_argument( + "--f-min", default=40.0, type=float, help="the minimum frequency", + ) + parser.add_argument( + "--min-level-db", + default=-100, + type=float, + help="the minimum db value for spectrogam normalization", + ) + parser.add_argument( + "--n-res-block", default=10, type=int, help="the number of ResBlock in stack", + ) + parser.add_argument( + "--n-rnn", default=512, type=int, help="the dimension of RNN layer", + ) + parser.add_argument( + "--n-fc", default=512, type=int, help="the dimension of fully connected layer", + ) + parser.add_argument( + "--kernel-size", + default=5, + type=int, + help="the number of kernel size in the first Conv1d layer", + ) + parser.add_argument( + "--n-freq", default=80, type=int, help="the number of spectrogram bins to use", + ) + parser.add_argument( + "--n-hidden-melresnet", + default=128, + type=int, + help="the number of hidden dimensions of resblock in melresnet", + ) + parser.add_argument( + "--n-output-melresnet", default=128, type=int, help="the output dimension of melresnet", + ) + parser.add_argument( + "--n-fft", default=2048, type=int, help="the number of Fourier bins", + ) + parser.add_argument( + "--loss", + default="crossentropy", + choices=["crossentropy", "mol"], + type=str, + help="the type of loss", + ) + parser.add_argument( + "--seq-len-factor", + default=5, + type=int, + help="the length of each waveform to process per batch = hop_length * seq_len_factor", + ) + parser.add_argument( + "--val-ratio", + default=0.1, + type=float, + help="the ratio of waveforms for validation", + ) + parser.add_argument( + "--file-path", default="", type=str, help="the path of audio files", + ) + + args = parser.parse_args() + return args + + +def train_one_epoch(model, criterion, optimizer, data_loader, device, epoch): + + model.train() + + sums = defaultdict(lambda: 0.0) + start1 = time() + + metric = MetricLogger("train_iteration") + metric["epoch"] = epoch + + for waveform, specgram, target in bg_iterator(data_loader, maxsize=2): + + start2 = time() + + waveform = waveform.to(device) + specgram = specgram.to(device) + target = target.to(device) + + output = model(waveform, specgram) + output, target = output.squeeze(1), target.squeeze(1) + + loss = criterion(output, target) + loss_item = loss.item() + sums["loss"] += loss_item + metric["loss"] = loss_item + + optimizer.zero_grad() + loss.backward() + + if args.clip_grad > 0: + gradient = torch.nn.utils.clip_grad_norm_( + model.parameters(), args.clip_grad + ) + sums["gradient"] += gradient.item() + metric["gradient"] = gradient.item() + + optimizer.step() + + metric["iteration"] = sums["iteration"] + metric["time"] = time() - start2 + metric() + sums["iteration"] += 1 + + avg_loss = sums["loss"] / len(data_loader) + + metric = MetricLogger("train_epoch") + metric["epoch"] = epoch + metric["loss"] = sums["loss"] / len(data_loader) + metric["gradient"] = avg_loss + metric["time"] = time() - start1 + metric() + + +def validate(model, criterion, data_loader, device, epoch): + + with torch.no_grad(): + + model.eval() + sums = defaultdict(lambda: 0.0) + start = time() + + for waveform, specgram, target in bg_iterator(data_loader, maxsize=2): + + waveform = waveform.to(device) + specgram = specgram.to(device) + target = target.to(device) + + output = model(waveform, specgram) + output, target = output.squeeze(1), target.squeeze(1) + + loss = criterion(output, target) + sums["loss"] += loss.item() + + avg_loss = sums["loss"] / len(data_loader) + + metric = MetricLogger("validation") + metric["epoch"] = epoch + metric["loss"] = avg_loss + metric["time"] = time() - start + metric() + + return avg_loss + + +def main(args): + + devices = ["cuda" if torch.cuda.is_available() else "cpu"] + + logging.info("Start time: {}".format(str(datetime.now()))) + + melkwargs = { + "n_fft": args.n_fft, + "power": 1, + "hop_length": args.hop_length, + "win_length": args.win_length, + } + + transforms = torch.nn.Sequential( + torchaudio.transforms.Spectrogram(**melkwargs), + LinearToMel( + sample_rate=args.sample_rate, + n_fft=args.n_fft, + n_mels=args.n_freq, + fmin=args.f_min, + ), + NormalizeDB(min_level_db=args.min_level_db), + ) + + train_dataset, val_dataset = split_process_ljspeech(args, transforms) + + loader_training_params = { + "num_workers": args.workers, + "pin_memory": False, + "shuffle": True, + "drop_last": False, + } + loader_validation_params = loader_training_params.copy() + loader_validation_params["shuffle"] = False + + collate_fn = collate_factory(args) + + train_loader = DataLoader( + train_dataset, + batch_size=args.batch_size, + collate_fn=collate_fn, + **loader_training_params, + ) + val_loader = DataLoader( + val_dataset, + batch_size=args.batch_size, + collate_fn=collate_fn, + **loader_validation_params, + ) + + n_classes = 2 ** args.n_bits if args.loss == "crossentropy" else 30 + + model = _WaveRNN( + upsample_scales=args.upsample_scales, + n_classes=n_classes, + hop_length=args.hop_length, + n_res_block=args.n_res_block, + n_rnn=args.n_rnn, + n_fc=args.n_fc, + kernel_size=args.kernel_size, + n_freq=args.n_freq, + n_hidden=args.n_hidden_melresnet, + n_output=args.n_output_melresnet, + ) + + if args.jit: + model = torch.jit.script(model) + + model = torch.nn.DataParallel(model) + model = model.to(devices[0], non_blocking=True) + + n = count_parameters(model) + logging.info(f"Number of parameters: {n}") + + # Optimizer + optimizer_params = { + "lr": args.learning_rate, + } + + optimizer = Adam(model.parameters(), **optimizer_params) + + criterion = LongCrossEntropyLoss() if args.loss == "crossentropy" else MoLLoss() + + best_loss = 10.0 + + if args.checkpoint and os.path.isfile(args.checkpoint): + logging.info(f"Checkpoint: loading '{args.checkpoint}'") + checkpoint = torch.load(args.checkpoint) + + args.start_epoch = checkpoint["epoch"] + best_loss = checkpoint["best_loss"] + + model.load_state_dict(checkpoint["state_dict"]) + optimizer.load_state_dict(checkpoint["optimizer"]) + + logging.info( + f"Checkpoint: loaded '{args.checkpoint}' at epoch {checkpoint['epoch']}" + ) + else: + logging.info("Checkpoint: not found") + + save_checkpoint( + { + "epoch": args.start_epoch, + "state_dict": model.state_dict(), + "best_loss": best_loss, + "optimizer": optimizer.state_dict(), + }, + False, + args.checkpoint, + ) + + for epoch in range(args.start_epoch, args.epochs): + + train_one_epoch( + model, criterion, optimizer, train_loader, devices[0], epoch, + ) + + if not (epoch + 1) % args.print_freq or epoch == args.epochs - 1: + + sum_loss = validate(model, criterion, val_loader, devices[0], epoch) + + is_best = sum_loss < best_loss + best_loss = min(sum_loss, best_loss) + save_checkpoint( + { + "epoch": epoch + 1, + "state_dict": model.state_dict(), + "best_loss": best_loss, + "optimizer": optimizer.state_dict(), + }, + is_best, + args.checkpoint, + ) + + logging.info(f"End time: {datetime.now()}") + + +if __name__ == "__main__": + + logging.basicConfig(level=logging.INFO) + args = parse_args() + main(args) diff --git a/examples/pipeline_wavernn/processing.py b/examples/pipeline_wavernn/processing.py new file mode 100644 index 0000000000..b22d60dae4 --- /dev/null +++ b/examples/pipeline_wavernn/processing.py @@ -0,0 +1,58 @@ +import librosa +import torch +import torch.nn as nn + + +# TODO Replace by torchaudio, once https://github.com/pytorch/audio/pull/593 is resolved +class LinearToMel(nn.Module): + def __init__(self, sample_rate, n_fft, n_mels, fmin, htk=False, norm="slaney"): + super().__init__() + self.sample_rate = sample_rate + self.n_fft = n_fft + self.n_mels = n_mels + self.fmin = fmin + self.htk = htk + self.norm = norm + + def forward(self, specgram): + specgram = librosa.feature.melspectrogram( + S=specgram.squeeze(0).numpy(), + sr=self.sample_rate, + n_fft=self.n_fft, + n_mels=self.n_mels, + fmin=self.fmin, + htk=self.htk, + norm=self.norm, + ) + return torch.from_numpy(specgram) + + +class NormalizeDB(nn.Module): + r"""Normalize the spectrogram with a minimum db value + """ + + def __init__(self, min_level_db): + super().__init__() + self.min_level_db = min_level_db + + def forward(self, specgram): + specgram = 20 * torch.log10(torch.clamp(specgram, min=1e-5)) + return torch.clamp( + (self.min_level_db - specgram) / self.min_level_db, min=0, max=1 + ) + + +def normalized_waveform_to_bits(waveform, bits): + r"""Transform waveform [-1, 1] to label [0, 2 ** bits - 1] + """ + + assert abs(waveform).max() <= 1.0 + waveform = (waveform + 1.0) * (2 ** bits - 1) / 2 + return torch.clamp(waveform, 0, 2 ** bits - 1).int() + + +def bits_to_normalized_waveform(label, bits): + r"""Transform label [0, 2 ** bits - 1] to waveform [-1, 1] + """ + + return 2 * label / (2 ** bits - 1.0) - 1.0 diff --git a/examples/pipeline_wavernn/utils.py b/examples/pipeline_wavernn/utils.py new file mode 100644 index 0000000000..e924c9f512 --- /dev/null +++ b/examples/pipeline_wavernn/utils.py @@ -0,0 +1,61 @@ +import logging +import os +import shutil +from collections import defaultdict, deque + +import torch + + +class MetricLogger: + r"""Logger for model metrics + """ + + def __init__(self, group, print_freq=1): + self.print_freq = print_freq + self._iter = 0 + self.data = defaultdict(lambda: deque(maxlen=self.print_freq)) + self.data["group"].append(group) + + def __setitem__(self, key, value): + self.data[key].append(value) + + def _get_last(self): + return {k: v[-1] for k, v in self.data.items()} + + def __str__(self): + return str(self._get_last()) + + def __call__(self): + self._iter = (self._iter + 1) % self.print_freq + if not self._iter: + print(self, flush=True) + + +def save_checkpoint(state, is_best, filename): + r"""Save the model to a temporary file first, + then copy it to filename, in case the signal interrupts + the torch.save() process. + """ + + if filename == "": + return + + tempfile = filename + ".temp" + + # Remove tempfile in case interuption during the copying from tempfile to filename + if os.path.isfile(tempfile): + os.remove(tempfile) + + torch.save(state, tempfile) + if os.path.isfile(tempfile): + os.rename(tempfile, filename) + if is_best: + shutil.copyfile(filename, "model_best.pth.tar") + logging.info("Checkpoint: saved") + + +def count_parameters(model): + r"""Count the total number of parameters in the model + """ + + return sum(p.numel() for p in model.parameters() if p.requires_grad) diff --git a/test/README.md b/test/README.md index 304bfa9c8e..c0a4b2a21d 100644 --- a/test/README.md +++ b/test/README.md @@ -29,7 +29,7 @@ The following test modules are defined for corresponding `torchaudio` module/fun - [`torchaudio.transforms`](./test_transforms.py) - [`torchaudio.compliance.kaldi`](./test_compliance_kaldi.py) - 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158fde87ed..beceb6cafb 100644 --- a/test/common_utils/backend_utils.py +++ b/test/common_utils/backend_utils.py @@ -29,8 +29,8 @@ def supports_mp3(backend): def set_audio_backend(backend): """Allow additional backend value, 'default'""" if backend == 'default': - if 'sox' in BACKENDS: - be = 'sox' + if 'sox_io' in BACKENDS: + be = 'sox_io' elif 'soundfile' in BACKENDS: be = 'soundfile' else: diff --git a/test/common_utils/case_utils.py b/test/common_utils/case_utils.py index f3b0c343a6..9c89ef6891 100644 --- a/test/common_utils/case_utils.py +++ b/test/common_utils/case_utils.py @@ -14,33 +14,28 @@ class TempDirMixin: """Mixin to provide easy access to temp dir""" temp_dir_ = None - base_temp_dir = None - temp_dir = None @classmethod - def setUpClass(cls): - super().setUpClass() + def get_base_temp_dir(cls): # If TORCHAUDIO_TEST_TEMP_DIR is set, use it instead of temporary directory. # this is handy for debugging. key = 'TORCHAUDIO_TEST_TEMP_DIR' if key in os.environ: - cls.base_temp_dir = os.environ[key] - else: + return os.environ[key] + if cls.temp_dir_ is None: cls.temp_dir_ = tempfile.TemporaryDirectory() - cls.base_temp_dir = cls.temp_dir_.name + return cls.temp_dir_.name @classmethod def tearDownClass(cls): super().tearDownClass() - if isinstance(cls.temp_dir_, tempfile.TemporaryDirectory): + if cls.temp_dir_ is not None: cls.temp_dir_.cleanup() - - def setUp(self): - super().setUp() - self.temp_dir = os.path.join(self.base_temp_dir, self.id()) + cls.temp_dir_ = None def get_temp_path(self, *paths): - path = os.path.join(self.temp_dir, *paths) + temp_dir = os.path.join(self.get_base_temp_dir(), self.id()) + path = os.path.join(temp_dir, *paths) os.makedirs(os.path.dirname(path), exist_ok=True) return path diff --git a/test/common_utils/data_utils.py b/test/common_utils/data_utils.py index b948ce334a..de19d8174f 100644 --- a/test/common_utils/data_utils.py +++ b/test/common_utils/data_utils.py @@ -13,6 +13,28 @@ def get_asset_path(*paths): return os.path.join(_TEST_DIR_PATH, 'assets', *paths) +def convert_tensor_encoding( + tensor: torch.tensor, + dtype: torch.dtype, +): + """Convert input tensor with values between -1 and 1 to integer encoding + Args: + tensor: input tensor, assumed between -1 and 1 + dtype: desired output tensor dtype + Returns: + Tensor: shape of (n_channels, sample_rate * duration) + """ + if dtype == torch.int32: + tensor *= (tensor > 0) * 2147483647 + (tensor < 0) * 2147483648 + if dtype == torch.int16: + tensor *= (tensor > 0) * 32767 + (tensor < 0) * 32768 + if dtype == torch.uint8: + tensor *= (tensor > 0) * 127 + (tensor < 0) * 128 + tensor += 128 + tensor = tensor.to(dtype) + return tensor + + def get_whitenoise( *, sample_rate: int = 16000, @@ -43,25 +65,17 @@ def get_whitenoise( if dtype not in [torch.float32, torch.int32, torch.int16, torch.uint8]: raise NotImplementedError(f'dtype {dtype} is not supported.') # According to the doc, folking rng on all CUDA devices is slow when there are many CUDA devices, - # so we only folk on CPU, generate values and move the data to the given device + # so we only fork on CPU, generate values and move the data to the given device with torch.random.fork_rng([]): torch.random.manual_seed(seed) - tensor = torch.randn([sample_rate * duration], dtype=torch.float32, device='cpu') + tensor = torch.randn([int(sample_rate * duration)], dtype=torch.float32, device='cpu') tensor /= 2.0 tensor *= scale_factor tensor.clamp_(-1.0, 1.0) - if dtype == torch.int32: - tensor *= (tensor > 0) * 2147483647 + (tensor < 0) * 2147483648 - if dtype == torch.int16: - tensor *= (tensor > 0) * 32767 + (tensor < 0) * 32768 - if dtype == torch.uint8: - tensor *= (tensor > 0) * 127 + (tensor < 0) * 128 - tensor += 128 - tensor = tensor.to(dtype) tensor = tensor.repeat([n_channels, 1]) if not channels_first: tensor = tensor.t() - return tensor.to(device=device) + return convert_tensor_encoding(tensor, dtype) def get_sinusoid( @@ -72,6 +86,7 @@ def get_sinusoid( n_channels: int = 1, dtype: Union[str, torch.dtype] = "float32", device: Union[str, torch.device] = "cpu", + channels_first: bool = True, ): """Generate pseudo audio data with sine wave. @@ -90,5 +105,8 @@ def get_sinusoid( dtype = getattr(torch, dtype) pie2 = 2 * 3.141592653589793 end = pie2 * frequency * duration - theta = torch.linspace(0, end, sample_rate * duration, dtype=dtype, device=device) - return torch.sin(theta, out=None).repeat([n_channels, 1]) + theta = torch.linspace(0, end, int(sample_rate * duration), dtype=torch.float32, device=device) + tensor = torch.sin(theta, out=None).repeat([n_channels, 1]) + if not channels_first: + tensor = tensor.t() + return convert_tensor_encoding(tensor, dtype) diff --git a/test/common_utils/sox_utils.py b/test/common_utils/sox_utils.py index cd1c247b72..db131cdec5 100644 --- a/test/common_utils/sox_utils.py +++ b/test/common_utils/sox_utils.py @@ -77,3 +77,24 @@ def convert_audio_file( command += [dst_path] print(' '.join(command)) subprocess.run(command, check=True) + + +def _flattern(effects): + if not effects: + return effects + if isinstance(effects[0], str): + return effects + return [item for sublist in effects for item in sublist] + + +def run_sox_effect(input_file, output_file, effect, *, output_sample_rate=None, output_bitdepth=None): + """Run sox effects""" + effect = _flattern(effect) + command = ['sox', '-V', '--no-dither', input_file] + if output_bitdepth: + command += ['--bits', str(output_bitdepth)] + command += [output_file] + effect + if output_sample_rate: + command += ['rate', str(output_sample_rate)] + print(' '.join(command)) + subprocess.run(command, check=True) diff --git a/test/datasets/__init__.py b/test/datasets/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/test/test_datasets.py b/test/datasets/datasets_test.py similarity index 71% rename from test/test_datasets.py rename to test/datasets/datasets_test.py index c3b0c917da..b8a980f346 100644 --- a/test/test_datasets.py +++ b/test/datasets/datasets_test.py @@ -1,25 +1,20 @@ -import unittest - from torchaudio.datasets.commonvoice import COMMONVOICE from torchaudio.datasets.librispeech import LIBRISPEECH from torchaudio.datasets.speechcommands import SPEECHCOMMANDS from torchaudio.datasets.utils import diskcache_iterator, bg_iterator from torchaudio.datasets.vctk import VCTK -from torchaudio.datasets.yesno import YESNO from torchaudio.datasets.ljspeech import LJSPEECH -from torchaudio.datasets.gtzan import GTZAN from torchaudio.datasets.cmuarctic import CMUARCTIC -from . import common_utils +from ..common_utils import ( + TorchaudioTestCase, + get_asset_path, +) -class TestDatasets(common_utils.TorchaudioTestCase): +class TestDatasets(TorchaudioTestCase): backend = 'default' - path = common_utils.get_asset_path() - - def test_yesno(self): - data = YESNO(self.path) - data[0] + path = get_asset_path() def test_vctk(self): data = VCTK(self.path) @@ -37,18 +32,14 @@ def test_speechcommands(self): data = SPEECHCOMMANDS(self.path) data[0] - def test_gtzan(self): - data = GTZAN(self.path) - data[0] - def test_cmuarctic(self): data = CMUARCTIC(self.path) data[0] -class TestCommonVoice(common_utils.TorchaudioTestCase): +class TestCommonVoice(TorchaudioTestCase): backend = 'default' - path = common_utils.get_asset_path() + path = get_asset_path() def test_commonvoice(self): data = COMMONVOICE(self.path, url="tatar") @@ -67,7 +58,3 @@ def test_commonvoice_bg(self): data = bg_iterator(data, 5) for _ in data: pass - - -if __name__ == "__main__": - unittest.main() diff --git a/test/datasets/gtzan_test.py b/test/datasets/gtzan_test.py new file mode 100644 index 0000000000..14c82a512a --- /dev/null +++ b/test/datasets/gtzan_test.py @@ -0,0 +1,88 @@ +import os + +from torchaudio.datasets import gtzan + +from ..common_utils import ( + TempDirMixin, + TorchaudioTestCase, + get_whitenoise, + save_wav, + normalize_wav, +) + + +class TestGTZAN(TempDirMixin, TorchaudioTestCase): + backend = 'default' + + root_dir = None + samples = [] + training = [] + validation = [] + testing = [] + + @classmethod + def setUpClass(cls): + cls.root_dir = cls.get_base_temp_dir() + sample_rate = 22050 + seed = 0 + for genre in gtzan.gtzan_genres: + base_dir = os.path.join(cls.root_dir, 'genres', genre) + os.makedirs(base_dir, exist_ok=True) + for i in range(100): + filename = f'{genre}.{i:05d}' + path = os.path.join(base_dir, f'{filename}.wav') + data = get_whitenoise(sample_rate=sample_rate, duration=0.01, n_channels=1, dtype='int16', seed=seed) + save_wav(path, data, sample_rate) + sample = (normalize_wav(data), sample_rate, genre) + cls.samples.append(sample) + if filename in gtzan.filtered_test: + cls.testing.append(sample) + if filename in gtzan.filtered_train: + cls.training.append(sample) + if filename in gtzan.filtered_valid: + cls.validation.append(sample) + seed += 1 + + def test_no_subset(self): + dataset = gtzan.GTZAN(self.root_dir) + + n_ite = 0 + for i, (waveform, sample_rate, label) in enumerate(dataset): + self.assertEqual(waveform, self.samples[i][0], atol=5e-5, rtol=1e-8) + assert sample_rate == self.samples[i][1] + assert label == self.samples[i][2] + n_ite += 1 + assert n_ite == len(self.samples) + + def test_training(self): + dataset = gtzan.GTZAN(self.root_dir, subset='training') + + n_ite = 0 + for i, (waveform, sample_rate, label) in enumerate(dataset): + self.assertEqual(waveform, self.training[i][0], atol=5e-5, rtol=1e-8) + assert sample_rate == self.training[i][1] + assert label == self.training[i][2] + n_ite += 1 + assert n_ite == len(self.training) + + def test_validation(self): + dataset = gtzan.GTZAN(self.root_dir, subset='validation') + + n_ite = 0 + for i, (waveform, sample_rate, label) in enumerate(dataset): + self.assertEqual(waveform, self.validation[i][0], atol=5e-5, rtol=1e-8) + assert sample_rate == self.validation[i][1] + assert label == self.validation[i][2] + n_ite += 1 + assert n_ite == len(self.validation) + + def test_testing(self): + dataset = gtzan.GTZAN(self.root_dir, subset='testing') + + n_ite = 0 + for i, (waveform, sample_rate, label) in enumerate(dataset): + self.assertEqual(waveform, self.testing[i][0], atol=5e-5, rtol=1e-8) + assert sample_rate == self.testing[i][1] + assert label == self.testing[i][2] + n_ite += 1 + assert n_ite == len(self.testing) diff --git a/test/datasets/libritts_test.py b/test/datasets/libritts_test.py new file mode 100644 index 0000000000..f31561d211 --- /dev/null +++ b/test/datasets/libritts_test.py @@ -0,0 +1,71 @@ +import os + +from torchaudio.datasets.libritts import LIBRITTS + +from ..common_utils import ( + TempDirMixin, + TorchaudioTestCase, + get_whitenoise, + save_wav, + normalize_wav, +) + + +class TestLibriTTS(TempDirMixin, TorchaudioTestCase): + backend = 'default' + + root_dir = None + data = [] + utterance_ids = [ + [19, 198, '000000', '000000'], + [26, 495, '000004', '000000'], + ] + original_text = 'this is the original text.' + normalized_text = 'this is the normalized text.' + + @classmethod + def setUpClass(cls): + cls.root_dir = cls.get_base_temp_dir() + base_dir = os.path.join(cls.root_dir, 'LibriTTS', 'train-clean-100') + for i, utterance_id in enumerate(cls.utterance_ids): + filename = f'{"_".join(str(u) for u in utterance_id)}.wav' + file_dir = os.path.join(base_dir, str(utterance_id[0]), str(utterance_id[1])) + os.makedirs(file_dir, exist_ok=True) + path = os.path.join(file_dir, filename) + + data = get_whitenoise(sample_rate=8000, duration=6, n_channels=1, dtype='int16', seed=i) + save_wav(path, data, 8000) + cls.data.append(normalize_wav(data)) + + original_text_filename = f'{"_".join(str(u) for u in utterance_id)}.original.txt' + path_original = os.path.join(file_dir, original_text_filename) + with open(path_original, 'w') as file_: + file_.write(cls.original_text) + + normalized_text_filename = f'{"_".join(str(u) for u in utterance_id)}.normalized.txt' + path_normalized = os.path.join(file_dir, normalized_text_filename) + with open(path_normalized, 'w') as file_: + file_.write(cls.normalized_text) + + def test_libritts(self): + dataset = LIBRITTS(self.root_dir) + n_ites = 0 + for i, (waveform, + sample_rate, + original_text, + normalized_text, + speaker_id, + chapter_id, + utterance_id) in enumerate(dataset): + + expected_ids = self.utterance_ids[i] + expected_data = self.data[i] + self.assertEqual(expected_data, waveform, atol=5e-5, rtol=1e-8) + assert sample_rate == 8000 + assert speaker_id == expected_ids[0] + assert chapter_id == expected_ids[1] + assert original_text == self.original_text + assert normalized_text == self.normalized_text + assert utterance_id == f'{"_".join(str(u) for u in expected_ids[-4:])}' + n_ites += 1 + assert n_ites == len(self.utterance_ids) diff --git a/test/datasets/utils_test.py b/test/datasets/utils_test.py new file mode 100644 index 0000000000..e77df09f4c --- /dev/null +++ b/test/datasets/utils_test.py @@ -0,0 +1,47 @@ +import os +from pathlib import Path + +from torchaudio.datasets import utils as dataset_utils + +from ..common_utils import ( + TempDirMixin, + TorchaudioTestCase, +) + + +class TestWalkFiles(TempDirMixin, TorchaudioTestCase): + root = None + expected = None + + def _add_file(self, *parts): + path = self.get_temp_path(*parts) + self.expected.append(path) + Path(path).touch() + + def setUp(self): + self.root = self.get_temp_path() + self.expected = [] + + # level 1 + for filename in ['a.txt', 'b.txt', 'c.txt']: + self._add_file(filename) + + # level 2 + for dir1 in ['d1', 'd2', 'd3']: + for filename in ['d.txt', 'e.txt', 'f.txt']: + self._add_file(dir1, filename) + # level 3 + for dir2 in ['d1', 'd2', 'd3']: + for filename in ['g.txt', 'h.txt', 'i.txt']: + self._add_file(dir1, dir2, filename) + + print('\n'.join(self.expected)) + + def test_walk_files(self): + """walk_files should traverse files in alphabetical order""" + n_ites = 0 + for i, path in enumerate(dataset_utils.walk_files(self.root, '.txt', prefix=True)): + found = os.path.join(self.root, path) + assert found == self.expected[i] + n_ites += 1 + assert n_ites == len(self.expected) diff --git a/test/datasets/yesno_test.py b/test/datasets/yesno_test.py new file mode 100644 index 0000000000..f9c4d0b051 --- /dev/null +++ b/test/datasets/yesno_test.py @@ -0,0 +1,49 @@ +import os + +from torchaudio.datasets import yesno + +from ..common_utils import ( + TempDirMixin, + TorchaudioTestCase, + get_whitenoise, + save_wav, + normalize_wav, +) + + +class TestYesNo(TempDirMixin, TorchaudioTestCase): + backend = 'default' + + root_dir = None + data = [] + labels = [ + [0, 0, 0, 0, 0, 0, 0, 0], + [0, 0, 0, 0, 1, 1, 1, 1], + [0, 1, 0, 1, 0, 1, 1, 0], + [1, 1, 1, 1, 0, 0, 0, 0], + [1, 1, 1, 1, 1, 1, 1, 1], + ] + + @classmethod + def setUpClass(cls): + cls.root_dir = cls.get_base_temp_dir() + base_dir = os.path.join(cls.root_dir, 'waves_yesno') + os.makedirs(base_dir, exist_ok=True) + for label in cls.labels: + filename = f'{"_".join(str(l) for l in label)}.wav' + path = os.path.join(base_dir, filename) + data = get_whitenoise(sample_rate=8000, duration=6, n_channels=1, dtype='int16') + save_wav(path, data, 8000) + cls.data.append(normalize_wav(data)) + + def test_yesno(self): + dataset = yesno.YESNO(self.root_dir) + n_ite = 0 + for i, (waveform, sample_rate, label) in enumerate(dataset): + expected_label = self.labels[i] + expected_data = self.data[i] + self.assertEqual(expected_data, waveform, atol=5e-5, rtol=1e-8) + assert sample_rate == 8000 + assert label == expected_label + n_ite += 1 + assert n_ite == len(self.data) diff --git a/test/functional_cpu_test.py b/test/functional_cpu_test.py index ab5fdaed95..b162df2ebc 100644 --- a/test/functional_cpu_test.py +++ b/test/functional_cpu_test.py @@ -4,6 +4,7 @@ import torch import torchaudio import torchaudio.functional as F +from parameterized import parameterized import pytest from . import common_utils @@ -299,24 +300,18 @@ def test_linearity_of_istft4(self): class TestDetectPitchFrequency(common_utils.TorchaudioTestCase): - def test_pitch(self): - test_filepath_100 = common_utils.get_asset_path("100Hz_44100Hz_16bit_05sec.wav") - test_filepath_440 = common_utils.get_asset_path("440Hz_44100Hz_16bit_05sec.wav") + @parameterized.expand([(100,), (440,)]) + def test_pitch(self, frequency): + sample_rate = 44100 + test_sine_waveform = common_utils.get_sinusoid( + frequency=frequency, sample_rate=sample_rate, duration=5, + ) - # Files from https://www.mediacollege.com/audio/tone/download/ - tests = [ - (test_filepath_100, 100), - (test_filepath_440, 440), - ] + freq = torchaudio.functional.detect_pitch_frequency(test_sine_waveform, sample_rate) - for filename, freq_ref in tests: - waveform, sample_rate = common_utils.load_wav(filename) - - freq = torchaudio.functional.detect_pitch_frequency(waveform, sample_rate) - - threshold = 1 - s = ((freq - freq_ref).abs() > threshold).sum() - self.assertFalse(s) + threshold = 1 + s = ((freq - frequency).abs() > threshold).sum() + self.assertFalse(s) class TestDB_to_amplitude(common_utils.TorchaudioTestCase): @@ -410,7 +405,3 @@ def test_mask_along_axis_iid(mask_param, mask_value, axis): assert mask_specgrams.size() == specgrams.size() assert (num_masked_columns < mask_param).sum() == num_masked_columns.numel() - - -if __name__ == '__main__': - unittest.main() diff --git a/test/sox_effect/__init__.py b/test/sox_effect/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/test/sox_effect/common.py b/test/sox_effect/common.py new file mode 100644 index 0000000000..86edcf6476 --- /dev/null +++ b/test/sox_effect/common.py @@ -0,0 +1,26 @@ +import json + +from parameterized import param + +from ..common_utils import get_asset_path + + +def name_func(func, _, params): + if isinstance(params.args[0], str): + args = "_".join([str(arg) for arg in params.args]) + else: + args = "_".join([str(arg) for arg in params.args[0]]) + return f'{func.__name__}_{args}' + + +def load_params(*paths): + params = [] + with open(get_asset_path(*paths), 'r') as file: + for line in file: + data = json.loads(line) + for effect in data['effects']: + for i, arg in enumerate(effect): + if arg.startswith(""): + effect[i] = arg.replace("", get_asset_path()) + params.append(param(data)) + return params diff --git a/test/sox_effect/smoke_test.py b/test/sox_effect/smoke_test.py new file mode 100644 index 0000000000..4997002170 --- /dev/null +++ b/test/sox_effect/smoke_test.py @@ -0,0 +1,59 @@ +from torchaudio import sox_effects +from parameterized import parameterized + +from ..common_utils import ( + TempDirMixin, + TorchaudioTestCase, + skipIfNoExtension, + get_wav_data, + get_sinusoid, + save_wav, +) +from .common import ( + name_func, + load_params, +) + + +@skipIfNoExtension +class SmokeTest(TempDirMixin, TorchaudioTestCase): + """Run smoke test on various effects + + The purpose of this test suite is to verify that sox_effect functionalities do not exhibit + abnormal behaviors. + + This test suite should be able to run without any additional tools (such as sox command), + however without such tools, the correctness of each function cannot be verified. + """ + @parameterized.expand( + load_params("sox_effect_test_args.json"), + name_func=lambda f, i, p: f'{f.__name__}_{i}_{p.args[0]["effects"][0][0]}', + ) + def test_apply_effects_tensor(self, args): + """`apply_effects_tensor` should not crash""" + effects = args['effects'] + num_channels = args.get("num_channels", 2) + input_sr = args.get("input_sample_rate", 8000) + original = get_sinusoid( + frequency=800, sample_rate=input_sr, + n_channels=num_channels, dtype='float32') + _found, _sr = sox_effects.apply_effects_tensor(original, input_sr, effects) + + @parameterized.expand( + load_params("sox_effect_test_args.json"), + name_func=lambda f, i, p: f'{f.__name__}_{i}_{p.args[0]["effects"][0][0]}', + ) + def test_apply_effects(self, args): + """`apply_effects_file` should return identical data as sox command""" + dtype = 'int32' + channels_first = True + effects = args['effects'] + num_channels = args.get("num_channels", 2) + input_sr = args.get("input_sample_rate", 8000) + + input_path = self.get_temp_path('input.wav') + data = get_wav_data(dtype, num_channels, channels_first=channels_first) + save_wav(input_path, data, input_sr, channels_first=channels_first) + + _found, _sr = sox_effects.apply_effects_file( + input_path, effects, normalize=False, channels_first=channels_first) diff --git a/test/test_sox_effects.py b/test/sox_effect/sox_effects_chain_test.py similarity index 82% rename from test/test_sox_effects.py rename to test/sox_effect/sox_effects_chain_test.py index 6b12d7cd06..765ab62094 100644 --- a/test/test_sox_effects.py +++ b/test/sox_effect/sox_effects_chain_test.py @@ -5,7 +5,7 @@ import torch import torchaudio -from . import common_utils +from .. import common_utils @common_utils.skipIfNoSoxBackend @@ -45,8 +45,8 @@ def test_lowpass_speed(self): E.append_effect_to_chain("speed", speed) E.append_effect_to_chain("rate", si.rate) x, sr = E.sox_build_flow_effects() - # check if effects worked - self.assertEqual(x.size(1), int((si.length / si.channels) / speed)) + # check if effects worked, add small tolerance for rounding effects + self.assertEqual(x.size(1), int((si.length / si.channels) / speed), atol=1, rtol=1e-8) def test_ulaw_and_siginfo(self): si_out = torchaudio.sox_signalinfo_t() @@ -222,55 +222,3 @@ def test_invalid_effect_options(self): E.append_effect_to_chain("compand", ["0.3", "1", "6:-70,-60,-20", "-5", "-90", "0.2"]) with self.assertRaises(RuntimeError): E.sox_build_flow_effects() - - def test_fade(self): - x_orig, _ = torchaudio.load(self.test_filepath) - fade_in_len = 44100 - fade_out_len = 44100 - - for fade_shape_sox, fade_shape_torchaudio in (("q", "quarter_sine"), ("h", "half_sine"), ("t", "linear")): - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.test_filepath) - E.append_effect_to_chain("fade", [fade_shape_sox, 1, "0", 1]) - x, sr = E.sox_build_flow_effects() - - fade = torchaudio.transforms.Fade(fade_in_len, fade_out_len, fade_shape_torchaudio) - - # check if effect worked - self.assertTrue(x.allclose(fade(x_orig), rtol=1e-4, atol=1e-4)) - - def test_vol(self): - x_orig, _ = torchaudio.load(self.test_filepath) - - for gain, gain_type in ((1.1, "amplitude"), (2, "db"), (2, "power")): - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.test_filepath) - E.append_effect_to_chain("vol", [gain, gain_type]) - x, sr = E.sox_build_flow_effects() - - vol = torchaudio.transforms.Vol(gain, gain_type) - z = vol(x_orig) - # check if effect worked - self.assertTrue(x.allclose(z, rtol=1e-4, atol=1e-4)) - - def test_vad(self): - sample_files = [ - common_utils.get_asset_path("vad-go-stereo-44100.wav"), - common_utils.get_asset_path("vad-go-mono-32000.wav") - ] - - for sample_file in sample_files: - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(sample_file) - E.append_effect_to_chain("vad") - x, _ = E.sox_build_flow_effects() - - x_orig, sample_rate = torchaudio.load(sample_file) - vad = torchaudio.transforms.Vad(sample_rate) - - y = vad(x_orig) - self.assertTrue(x.allclose(y, rtol=1e-4, atol=1e-4)) - - -if __name__ == '__main__': - unittest.main() diff --git a/test/sox_effect/test_dataset.py b/test/sox_effect/test_dataset.py new file mode 100644 index 0000000000..f1b71eb806 --- /dev/null +++ b/test/sox_effect/test_dataset.py @@ -0,0 +1,114 @@ +from typing import List, Tuple + +import numpy as np +import torch +import torchaudio + +from ..common_utils import ( + TempDirMixin, + PytorchTestCase, + skipIfNoExtension, + get_whitenoise, + save_wav, +) + + +class RandomPerturbationFile(torch.utils.data.Dataset): + """Given flist, apply random speed perturbation""" + def __init__(self, flist: List[str], sample_rate: int): + super().__init__() + self.flist = flist + self.sample_rate = sample_rate + self.rng = None + + def __getitem__(self, index): + speed = self.rng.uniform(0.5, 2.0) + effects = [ + ['gain', '-n', '-10'], + ['speed', f'{speed:.5f}'], # duration of data is 0.5 ~ 2.0 seconds. + ['rate', f'{self.sample_rate}'], + ['pad', '0', '1.5'], # add 1.5 seconds silence at the end + ['trim', '0', '2'], # get the first 2 seconds + ] + data, _ = torchaudio.sox_effects.apply_effects_file(self.flist[index], effects) + return data + + def __len__(self): + return len(self.flist) + + +class RandomPerturbationTensor(torch.utils.data.Dataset): + """Apply speed purturbation to (synthetic) Tensor data""" + def __init__(self, signals: List[Tuple[torch.Tensor, int]], sample_rate: int): + super().__init__() + self.signals = signals + self.sample_rate = sample_rate + self.rng = None + + def __getitem__(self, index): + speed = self.rng.uniform(0.5, 2.0) + effects = [ + ['gain', '-n', '-10'], + ['speed', f'{speed:.5f}'], # duration of data is 0.5 ~ 2.0 seconds. + ['rate', f'{self.sample_rate}'], + ['pad', '0', '1.5'], # add 1.5 seconds silence at the end + ['trim', '0', '2'], # get the first 2 seconds + ] + tensor, sample_rate = self.signals[index] + data, _ = torchaudio.sox_effects.apply_effects_tensor(tensor, sample_rate, effects) + return data + + def __len__(self): + return len(self.signals) + + +def init_random_seed(worker_id): + dataset = torch.utils.data.get_worker_info().dataset + dataset.rng = np.random.RandomState(worker_id) + + +@skipIfNoExtension +class TestSoxEffectsDataset(TempDirMixin, PytorchTestCase): + """Test `apply_effects_file` in multi-process dataloader setting""" + + def _generate_dataset(self, num_samples=128): + flist = [] + for i in range(num_samples): + sample_rate = np.random.choice([8000, 16000, 44100]) + dtype = np.random.choice(['float32', 'int32', 'int16', 'uint8']) + data = get_whitenoise(n_channels=2, sample_rate=sample_rate, duration=1, dtype=dtype) + path = self.get_temp_path(f'{i:03d}_{dtype}_{sample_rate}.wav') + save_wav(path, data, sample_rate) + flist.append(path) + return flist + + def test_apply_effects_file(self): + sample_rate = 12000 + flist = self._generate_dataset() + dataset = RandomPerturbationFile(flist, sample_rate) + loader = torch.utils.data.DataLoader( + dataset, batch_size=32, num_workers=16, + worker_init_fn=init_random_seed, + ) + for batch in loader: + assert batch.shape == (32, 2, 2 * sample_rate) + + def _generate_signals(self, num_samples=128): + signals = [] + for _ in range(num_samples): + sample_rate = np.random.choice([8000, 16000, 44100]) + data = get_whitenoise( + n_channels=2, sample_rate=sample_rate, duration=1, dtype='float32') + signals.append((data, sample_rate)) + return signals + + def test_apply_effects_tensor(self): + sample_rate = 12000 + signals = self._generate_signals() + dataset = RandomPerturbationTensor(signals, sample_rate) + loader = torch.utils.data.DataLoader( + dataset, batch_size=32, num_workers=16, + worker_init_fn=init_random_seed, + ) + for batch in loader: + assert batch.shape == (32, 2, 2 * sample_rate) diff --git a/test/sox_effect/test_sox_effect.py b/test/sox_effect/test_sox_effect.py new file mode 100644 index 0000000000..38ab0fa772 --- /dev/null +++ b/test/sox_effect/test_sox_effect.py @@ -0,0 +1,221 @@ +import itertools + +from torchaudio import sox_effects +from parameterized import parameterized + +from ..common_utils import ( + TempDirMixin, + PytorchTestCase, + skipIfNoExtension, + get_sinusoid, + get_wav_data, + save_wav, + load_wav, + sox_utils, +) +from .common import ( + load_params, + name_func, +) + + +@skipIfNoExtension +class TestSoxEffects(PytorchTestCase): + def test_init(self): + """Calling init_sox_effects multiple times does not crush""" + for _ in range(3): + sox_effects.init_sox_effects() + + +@skipIfNoExtension +class TestSoxEffectsTensor(TempDirMixin, PytorchTestCase): + """Test suite for `apply_effects_tensor` function""" + @parameterized.expand(list(itertools.product( + ['float32', 'int32', 'int16', 'uint8'], + [8000, 16000], + [1, 2, 4, 8], + [True, False] + )), name_func=name_func) + def test_apply_no_effect(self, dtype, sample_rate, num_channels, channels_first): + """`apply_effects_tensor` without effects should return identical data as input""" + original = get_wav_data(dtype, num_channels, channels_first=channels_first) + expected = original.clone() + found, output_sample_rate = sox_effects.apply_effects_tensor( + expected, sample_rate, [], channels_first) + + assert output_sample_rate == sample_rate + # SoxEffect should not alter the input Tensor object + self.assertEqual(original, expected) + # SoxEffect should not return the same Tensor object + assert expected is not found + # Returned Tensor should equal to the input Tensor + self.assertEqual(expected, found) + + @parameterized.expand( + load_params("sox_effect_test_args.json"), + name_func=lambda f, i, p: f'{f.__name__}_{i}_{p.args[0]["effects"][0][0]}', + ) + def test_apply_effects(self, args): + """`apply_effects_tensor` should return identical data as sox command""" + effects = args['effects'] + num_channels = args.get("num_channels", 2) + input_sr = args.get("input_sample_rate", 8000) + output_sr = args.get("output_sample_rate") + + input_path = self.get_temp_path('input.wav') + reference_path = self.get_temp_path('reference.wav') + + original = get_sinusoid( + frequency=800, sample_rate=input_sr, + n_channels=num_channels, dtype='float32') + save_wav(input_path, original, input_sr) + sox_utils.run_sox_effect( + input_path, reference_path, effects, output_sample_rate=output_sr) + + expected, expected_sr = load_wav(reference_path) + found, sr = sox_effects.apply_effects_tensor(original, input_sr, effects) + + assert sr == expected_sr + self.assertEqual(expected, found) + + +@skipIfNoExtension +class TestSoxEffectsFile(TempDirMixin, PytorchTestCase): + """Test suite for `apply_effects_file` function""" + @parameterized.expand(list(itertools.product( + ['float32', 'int32', 'int16', 'uint8'], + [8000, 16000], + [1, 2, 4, 8], + [False, True], + )), name_func=name_func) + def test_apply_no_effect(self, dtype, sample_rate, num_channels, channels_first): + """`apply_effects_file` without effects should return identical data as input""" + path = self.get_temp_path('input.wav') + expected = get_wav_data(dtype, num_channels, channels_first=channels_first) + save_wav(path, expected, sample_rate, channels_first=channels_first) + + found, output_sample_rate = sox_effects.apply_effects_file( + path, [], normalize=False, channels_first=channels_first) + + assert output_sample_rate == sample_rate + self.assertEqual(expected, found) + + @parameterized.expand( + load_params("sox_effect_test_args.json"), + name_func=lambda f, i, p: f'{f.__name__}_{i}_{p.args[0]["effects"][0][0]}', + ) + def test_apply_effects(self, args): + """`apply_effects_file` should return identical data as sox command""" + dtype = 'int32' + channels_first = True + effects = args['effects'] + num_channels = args.get("num_channels", 2) + input_sr = args.get("input_sample_rate", 8000) + output_sr = args.get("output_sample_rate") + + input_path = self.get_temp_path('input.wav') + reference_path = self.get_temp_path('reference.wav') + data = get_wav_data(dtype, num_channels, channels_first=channels_first) + save_wav(input_path, data, input_sr, channels_first=channels_first) + sox_utils.run_sox_effect( + input_path, reference_path, effects, output_sample_rate=output_sr) + + expected, expected_sr = load_wav(reference_path) + found, sr = sox_effects.apply_effects_file( + input_path, effects, normalize=False, channels_first=channels_first) + + assert sr == expected_sr + self.assertEqual(found, expected) + + +@skipIfNoExtension +class TestFileFormats(TempDirMixin, PytorchTestCase): + """`apply_effects_file` gives the same result as sox on various file formats""" + @parameterized.expand(list(itertools.product( + ['float32', 'int32', 'int16', 'uint8'], + [8000, 16000], + [1, 2], + )), name_func=lambda f, _, p: f'{f.__name__}_{"_".join(str(arg) for arg in p.args)}') + def test_wav(self, dtype, sample_rate, num_channels): + """`apply_effects_file` works on various wav format""" + channels_first = True + effects = [['band', '300', '10']] + + input_path = self.get_temp_path('input.wav') + reference_path = self.get_temp_path('reference.wav') + data = get_wav_data(dtype, num_channels, channels_first=channels_first) + save_wav(input_path, data, sample_rate, channels_first=channels_first) + sox_utils.run_sox_effect(input_path, reference_path, effects) + + expected, expected_sr = load_wav(reference_path) + found, sr = sox_effects.apply_effects_file( + input_path, effects, normalize=False, channels_first=channels_first) + + assert sr == expected_sr + self.assertEqual(found, expected) + + @parameterized.expand(list(itertools.product( + [8000, 16000], + [1, 2], + )), name_func=lambda f, _, p: f'{f.__name__}_{"_".join(str(arg) for arg in p.args)}') + def test_mp3(self, sample_rate, num_channels): + """`apply_effects_file` works on various mp3 format""" + channels_first = True + effects = [['band', '300', '10']] + + input_path = self.get_temp_path('input.mp3') + reference_path = self.get_temp_path('reference.wav') + sox_utils.gen_audio_file(input_path, sample_rate, num_channels) + sox_utils.run_sox_effect(input_path, reference_path, effects) + + expected, expected_sr = load_wav(reference_path) + found, sr = sox_effects.apply_effects_file( + input_path, effects, channels_first=channels_first) + save_wav(self.get_temp_path('result.wav'), found, sr, channels_first=channels_first) + + assert sr == expected_sr + self.assertEqual(found, expected, atol=1e-4, rtol=1e-8) + + @parameterized.expand(list(itertools.product( + [8000, 16000], + [1, 2], + )), name_func=lambda f, _, p: f'{f.__name__}_{"_".join(str(arg) for arg in p.args)}') + def test_flac(self, sample_rate, num_channels): + """`apply_effects_file` works on various flac format""" + channels_first = True + effects = [['band', '300', '10']] + + input_path = self.get_temp_path('input.flac') + reference_path = self.get_temp_path('reference.wav') + sox_utils.gen_audio_file(input_path, sample_rate, num_channels) + sox_utils.run_sox_effect(input_path, reference_path, effects, output_bitdepth=32) + + expected, expected_sr = load_wav(reference_path) + found, sr = sox_effects.apply_effects_file( + input_path, effects, channels_first=channels_first) + save_wav(self.get_temp_path('result.wav'), found, sr, channels_first=channels_first) + + assert sr == expected_sr + self.assertEqual(found, expected) + + @parameterized.expand(list(itertools.product( + [8000, 16000], + [1, 2], + )), name_func=lambda f, _, p: f'{f.__name__}_{"_".join(str(arg) for arg in p.args)}') + def test_vorbis(self, sample_rate, num_channels): + """`apply_effects_file` works on various vorbis format""" + channels_first = True + effects = [['band', '300', '10']] + + input_path = self.get_temp_path('input.vorbis') + reference_path = self.get_temp_path('reference.wav') + sox_utils.gen_audio_file(input_path, sample_rate, num_channels) + sox_utils.run_sox_effect(input_path, reference_path, effects, output_bitdepth=32) + + expected, expected_sr = load_wav(reference_path) + found, sr = sox_effects.apply_effects_file( + input_path, effects, channels_first=channels_first) + save_wav(self.get_temp_path('result.wav'), found, sr, channels_first=channels_first) + + assert sr == expected_sr + self.assertEqual(found, expected) diff --git a/test/sox_effect/test_torchscript.py b/test/sox_effect/test_torchscript.py new file mode 100644 index 0000000000..ded923ab11 --- /dev/null +++ b/test/sox_effect/test_torchscript.py @@ -0,0 +1,100 @@ +from typing import List + +import torch +from torchaudio import sox_effects +from parameterized import parameterized + +from ..common_utils import ( + TempDirMixin, + PytorchTestCase, + skipIfNoExtension, + get_sinusoid, + save_wav, +) +from .common import ( + load_params, +) + + +class SoxEffectTensorTransform(torch.nn.Module): + effects: List[List[str]] + + def __init__(self, effects: List[List[str]], sample_rate: int, channels_first: bool): + super().__init__() + self.effects = effects + self.sample_rate = sample_rate + self.channels_first = channels_first + + def forward(self, tensor: torch.Tensor): + return sox_effects.apply_effects_tensor( + tensor, self.sample_rate, self.effects, self.channels_first) + + +class SoxEffectFileTransform(torch.nn.Module): + effects: List[List[str]] + channels_first: bool + + def __init__(self, effects: List[List[str]], channels_first: bool): + super().__init__() + self.effects = effects + self.channels_first = channels_first + + def forward(self, path: str): + return sox_effects.apply_effects_file(path, self.effects, self.channels_first) + + +@skipIfNoExtension +class TestTorchScript(TempDirMixin, PytorchTestCase): + @parameterized.expand( + load_params("sox_effect_test_args.json"), + name_func=lambda f, i, p: f'{f.__name__}_{i}_{p.args[0]["effects"][0][0]}', + ) + def test_apply_effects_tensor(self, args): + effects = args['effects'] + channels_first = True + num_channels = args.get("num_channels", 2) + input_sr = args.get("input_sample_rate", 8000) + + trans = SoxEffectTensorTransform(effects, input_sr, channels_first) + + path = self.get_temp_path('sox_effect.zip') + torch.jit.script(trans).save(path) + trans = torch.jit.load(path) + + wav = get_sinusoid( + frequency=800, sample_rate=input_sr, + n_channels=num_channels, dtype='float32', channels_first=channels_first) + found, sr_found = trans(wav) + expected, sr_expected = sox_effects.apply_effects_tensor( + wav, input_sr, effects, channels_first) + + assert sr_found == sr_expected + self.assertEqual(expected, found) + + @parameterized.expand( + load_params("sox_effect_test_args.json"), + name_func=lambda f, i, p: f'{f.__name__}_{i}_{p.args[0]["effects"][0][0]}', + ) + def test_apply_effects_file(self, args): + effects = args['effects'] + channels_first = True + num_channels = args.get("num_channels", 2) + input_sr = args.get("input_sample_rate", 8000) + + trans = SoxEffectFileTransform(effects, channels_first) + + path = self.get_temp_path('sox_effect.zip') + torch.jit.script(trans).save(path) + trans = torch.jit.load(path) + + path = self.get_temp_path('input.wav') + wav = get_sinusoid( + frequency=800, sample_rate=input_sr, + n_channels=num_channels, dtype='float32', channels_first=channels_first) + save_wav(path, wav, sample_rate=input_sr, channels_first=channels_first) + + found, sr_found = trans(path) + expected, sr_expected = sox_effects.apply_effects_file(path, effects, channels_first) + + assert sr_found == sr_expected + self.assertEqual(expected, found) diff --git a/test/sox_io_backend/smoke_test.py b/test/sox_io_backend/smoke_test.py new file mode 100644 index 0000000000..1ae9a8d938 --- /dev/null +++ b/test/sox_io_backend/smoke_test.py @@ -0,0 +1,87 @@ +import itertools +import unittest + +from torchaudio.utils import sox_utils +from torchaudio.backend import sox_io_backend +from torchaudio._internal.module_utils import is_module_available +from parameterized import parameterized + +from ..common_utils import ( + TempDirMixin, + TorchaudioTestCase, + skipIfNoExtension, + get_wav_data, +) +from .common import name_func + + +skipIfNoMP3 = unittest.skipIf( + not is_module_available('torchaudio._torchaudio') or + 'mp3' not in sox_utils.list_read_formats() or + 'mp3' not in sox_utils.list_write_formats(), + '"sox_io" backend does not support MP3') + + +@skipIfNoExtension +class SmokeTest(TempDirMixin, TorchaudioTestCase): + """Run smoke test on various audio format + + The purpose of this test suite is to verify that sox_io_backend functionalities do not exhibit + abnormal behaviors. + + This test suite should be able to run without any additional tools (such as sox command), + however without such tools, the correctness of each function cannot be verified. + """ + def run_smoke_test(self, ext, sample_rate, num_channels, *, compression=None, dtype='float32'): + duration = 1 + num_frames = sample_rate * duration + path = self.get_temp_path(f'test.{ext}') + original = get_wav_data(dtype, num_channels, normalize=False, num_frames=num_frames) + + # 1. run save + sox_io_backend.save(path, original, sample_rate, compression=compression) + # 2. run info + info = sox_io_backend.info(path) + assert info.sample_rate == sample_rate + assert info.num_channels == num_channels + # 3. run load + loaded, sr = sox_io_backend.load(path, normalize=False) + assert sr == sample_rate + assert loaded.shape[0] == num_channels + + @parameterized.expand(list(itertools.product( + ['float32', 'int32', 'int16', 'uint8'], + [8000, 16000], + [1, 2], + )), name_func=name_func) + def test_wav(self, dtype, sample_rate, num_channels): + """Run smoke test on wav format""" + self.run_smoke_test('wav', sample_rate, num_channels, dtype=dtype) + + @parameterized.expand(list(itertools.product( + [8000, 16000], + [1, 2], + [-4.2, -0.2, 0, 0.2, 96, 128, 160, 192, 224, 256, 320], + ))) + @skipIfNoMP3 + def test_mp3(self, sample_rate, num_channels, bit_rate): + """Run smoke test on mp3 format""" + self.run_smoke_test('mp3', sample_rate, num_channels, compression=bit_rate) + + @parameterized.expand(list(itertools.product( + [8000, 16000], + [1, 2], + [-1, 0, 1, 2, 3, 3.6, 5, 10], + ))) + def test_vorbis(self, sample_rate, num_channels, quality_level): + """Run smoke test on vorbis format""" + self.run_smoke_test('vorbis', sample_rate, num_channels, compression=quality_level) + + @parameterized.expand(list(itertools.product( + [8000, 16000], + [1, 2], + list(range(9)), + )), name_func=name_func) + def test_flac(self, sample_rate, num_channels, compression_level): + """Run smoke test on flac format""" + self.run_smoke_test('flac', sample_rate, num_channels, compression=compression_level) diff --git a/test/test_batch_consistency.py b/test/test_batch_consistency.py index 2a88598f88..e7040fdc0b 100644 --- a/test/test_batch_consistency.py +++ b/test/test_batch_consistency.py @@ -1,5 +1,7 @@ """Test numerical consistency among single input and batched input.""" import unittest +import itertools +from parameterized import parameterized import torch import torchaudio @@ -47,17 +49,15 @@ def test_griffinlim(self): F.griffinlim, tensor, window, n_fft, hop, ws, power, normalize, n_iter, momentum, length, 0, atol=5e-5 ) - def test_detect_pitch_frequency(self): - filenames = [ - 'steam-train-whistle-daniel_simon.wav', # 2ch 44100Hz - # Files from https://www.mediacollege.com/audio/tone/download/ - '100Hz_44100Hz_16bit_05sec.wav', # 1ch - '440Hz_44100Hz_16bit_05sec.wav', # 1ch - ] - for filename in filenames: - filepath = common_utils.get_asset_path(filename) - waveform, sample_rate = torchaudio.load(filepath) - self.assert_batch_consistencies(F.detect_pitch_frequency, waveform, sample_rate) + @parameterized.expand(list(itertools.product( + [100, 440], + [8000, 16000, 44100], + [1, 2], + )), name_func=lambda f, _, p: f'{f.__name__}_{"_".join(str(arg) for arg in p.args)}') + def test_detect_pitch_frequency(self, frequency, sample_rate, n_channels): + waveform = common_utils.get_sinusoid(frequency=frequency, sample_rate=sample_rate, + n_channels=n_channels, duration=5) + self.assert_batch_consistencies(F.detect_pitch_frequency, waveform, sample_rate) def test_istft(self): stft = torch.tensor([ @@ -80,8 +80,10 @@ def test_overdrive(self): self.assert_batch_consistencies(F.overdrive, waveform, gain=45, colour=30) def test_phaser(self): - filepath = common_utils.get_asset_path("whitenoise.wav") - waveform, sample_rate = torchaudio.load(filepath) + sample_rate = 44100 + waveform = common_utils.get_whitenoise( + sample_rate=sample_rate, duration=5, + ) self.assert_batch_consistencies(F.phaser, waveform, sample_rate) def test_flanger(self): @@ -286,7 +288,3 @@ def test_batch_Vol(self): # Batch then transform computed = torchaudio.transforms.Vol(gain=1.1)(waveform.repeat(3, 1, 1)) self.assertEqual(computed, expected) - - -if __name__ == '__main__': - unittest.main() diff --git a/test/test_compliance_kaldi.py b/test/test_compliance_kaldi.py index f9e097e09c..450183d6b3 100644 --- a/test/test_compliance_kaldi.py +++ b/test/test_compliance_kaldi.py @@ -47,14 +47,25 @@ def first_sample_of_frame(frame, window_size, window_shift, snip_edges): @common_utils.skipIfNoSoxBackend -class Test_Kaldi(common_utils.TorchaudioTestCase): +class Test_Kaldi(common_utils.TempDirMixin, common_utils.TorchaudioTestCase): backend = 'sox' - test_filepath = common_utils.get_asset_path('kaldi_file.wav') - test_8000_filepath = common_utils.get_asset_path('kaldi_file_8000.wav') kaldi_output_dir = common_utils.get_asset_path('kaldi') + test_filepath = common_utils.get_asset_path('kaldi_file.wav') test_filepaths = {prefix: [] for prefix in compliance_utils.TEST_PREFIX} + def setUp(self): + super().setUp() + + # 1. test signal for testing resampling + self.test1_signal_sr = 16000 + self.test1_signal = common_utils.get_whitenoise( + sample_rate=self.test1_signal_sr, duration=0.5, + ) + + # 2. test audio file corresponding to saved kaldi ark files + self.test2_filepath = common_utils.get_asset_path('kaldi_file_8000.wav') + # separating test files by their types (e.g 'spec', 'fbank', etc.) for f in os.listdir(kaldi_output_dir): dash_idx = f.find('-') @@ -94,7 +105,6 @@ def test_get_strided(self): def _create_data_set(self): # used to generate the dataset to test on. this is not used in testing (offline procedure) - test_filepath = common_utils.get_asset_path('kaldi_file.wav') sr = 16000 x = torch.arange(0, 20).float() # between [-6,6] @@ -103,8 +113,8 @@ def _create_data_set(self): y = (y / 6 * (1 << 30)).long() # clear the last 16 bits because they aren't used anyways y = ((y >> 16) << 16).float() - torchaudio.save(test_filepath, y, sr) - sound, sample_rate = torchaudio.load(test_filepath, normalization=False) + torchaudio.save(self.test_filepath, y, sr) + sound, sample_rate = torchaudio.load(self.test_filepath, normalization=False) print(y >> 16) self.assertTrue(sample_rate == sr) torch.testing.assert_allclose(y, sound) @@ -123,7 +133,7 @@ def _print_diagnostic(self, output, expect_output): print('relative_mse:', relative_mse.item(), 'relative_max_error:', relative_max_error.item()) def _compliance_test_helper(self, sound_filepath, filepath_key, expected_num_files, - expected_num_args, get_output_fn, atol=1e-5, rtol=1e-8): + expected_num_args, get_output_fn, atol=1e-5, rtol=1e-7): """ Inputs: sound_filepath (str): The location of the sound file @@ -135,7 +145,7 @@ def _compliance_test_helper(self, sound_filepath, filepath_key, expected_num_fil atol (float): absolute tolerance rtol (float): relative tolerance """ - sound, sample_rate = torchaudio.load_wav(sound_filepath) + sound, sr = torchaudio.load_wav(sound_filepath) files = self.test_filepaths[filepath_key] assert len(files) == expected_num_files, ('number of kaldi %s file changed to %d' % (filepath_key, len(files))) @@ -170,22 +180,19 @@ def get_output_fn(sound, args): output = kaldi.resample_waveform(sound, args[1], args[2]) return output - self._compliance_test_helper(self.test_8000_filepath, 'resample', 32, 3, get_output_fn, atol=1e-2, rtol=1e-5) + self._compliance_test_helper(self.test2_filepath, 'resample', 32, 3, get_output_fn, atol=1e-2, rtol=1e-5) def test_resample_waveform_upsample_size(self): - sound, sample_rate = torchaudio.load_wav(self.test_8000_filepath) - upsample_sound = kaldi.resample_waveform(sound, sample_rate, sample_rate * 2) - self.assertTrue(upsample_sound.size(-1) == sound.size(-1) * 2) + upsample_sound = kaldi.resample_waveform(self.test1_signal, self.test1_signal_sr, self.test1_signal_sr * 2) + self.assertTrue(upsample_sound.size(-1) == self.test1_signal.size(-1) * 2) def test_resample_waveform_downsample_size(self): - sound, sample_rate = torchaudio.load_wav(self.test_8000_filepath) - downsample_sound = kaldi.resample_waveform(sound, sample_rate, sample_rate // 2) - self.assertTrue(downsample_sound.size(-1) == sound.size(-1) // 2) + downsample_sound = kaldi.resample_waveform(self.test1_signal, self.test1_signal_sr, self.test1_signal_sr // 2) + self.assertTrue(downsample_sound.size(-1) == self.test1_signal.size(-1) // 2) def test_resample_waveform_identity_size(self): - sound, sample_rate = torchaudio.load_wav(self.test_8000_filepath) - downsample_sound = kaldi.resample_waveform(sound, sample_rate, sample_rate) - self.assertTrue(downsample_sound.size(-1) == sound.size(-1)) + downsample_sound = kaldi.resample_waveform(self.test1_signal, self.test1_signal_sr, self.test1_signal_sr) + self.assertTrue(downsample_sound.size(-1) == self.test1_signal.size(-1)) def _test_resample_waveform_accuracy(self, up_scale_factor=None, down_scale_factor=None, atol=1e-1, rtol=1e-4): @@ -226,20 +233,16 @@ def test_resample_waveform_upsample_accuracy(self): def test_resample_waveform_multi_channel(self): num_channels = 3 - sound, sample_rate = torchaudio.load_wav(self.test_8000_filepath) # (1, 8000) - multi_sound = sound.repeat(num_channels, 1) # (num_channels, 8000) + multi_sound = self.test1_signal.repeat(num_channels, 1) # (num_channels, 8000 smp) for i in range(num_channels): multi_sound[i, :] *= (i + 1) * 1.5 - multi_sound_sampled = kaldi.resample_waveform(multi_sound, sample_rate, sample_rate // 2) + multi_sound_sampled = kaldi.resample_waveform(multi_sound, self.test1_signal_sr, self.test1_signal_sr // 2) # check that sampling is same whether using separately or in a tensor of size (c, n) for i in range(num_channels): - single_channel = sound * (i + 1) * 1.5 - single_channel_sampled = kaldi.resample_waveform(single_channel, sample_rate, sample_rate // 2) - torch.testing.assert_allclose(multi_sound_sampled[i, :], single_channel_sampled[0], rtol=1e-4, atol=1e-8) - - -if __name__ == '__main__': - unittest.main() + single_channel = self.test1_signal * (i + 1) * 1.5 + single_channel_sampled = kaldi.resample_waveform(single_channel, self.test1_signal_sr, + self.test1_signal_sr // 2) + torch.testing.assert_allclose(multi_sound_sampled[i, :], single_channel_sampled[0], rtol=1e-4, atol=1e-7) diff --git a/test/test_dataloader.py b/test/test_dataloader.py index 1bf73ebb64..6d1518b70c 100644 --- a/test/test_dataloader.py +++ b/test/test_dataloader.py @@ -37,7 +37,3 @@ def test_1(self): dl = DataLoader(ds, batch_size=2) for x in dl: self.assertTrue(x.size() == expected_size) - - -if __name__ == '__main__': - unittest.main() diff --git a/test/test_io.py b/test/test_io.py index 2c8aece85e..b55885059a 100644 --- a/test/test_io.py +++ b/test/test_io.py @@ -282,6 +282,3 @@ def _test_5_get_info(self): self.assertEqual(si.length, samples) self.assertEqual(si.rate, rate) self.assertEqual(ei.bits_per_sample, precision) - -if __name__ == '__main__': - unittest.main() diff --git a/test/test_kaldi_io.py b/test/test_kaldi_io.py index 4bd0fd56a4..80e6b3dc5a 100644 --- a/test/test_kaldi_io.py +++ b/test/test_kaldi_io.py @@ -33,7 +33,3 @@ def test_read_vec_flt_ark(self): def test_read_mat_ark(self): self._test_helper("mat.ark", [self.data1, self.data2], kio.read_mat_ark, torch.float32) - - -if __name__ == '__main__': - unittest.main() diff --git a/test/test_librosa_compatibility.py b/test/test_librosa_compatibility.py index aa933535e8..01b0f23732 100644 --- a/test/test_librosa_compatibility.py +++ b/test/test_librosa_compatibility.py @@ -270,16 +270,13 @@ def test_basics4(self): } self.assert_compatibilities(**kwargs) - @unittest.skipIf(not common_utils.BACKENDS_MP3, 'no backend to read mp3') def test_MelScale(self): """MelScale transform is comparable to that of librosa""" n_fft = 2048 n_mels = 256 hop_length = n_fft // 4 - - # Prepare spectrogram input. We use torchaudio to compute one. - common_utils.set_audio_backend('default') - sound, sample_rate = _load_audio_asset('whitenoise_1min.mp3') + sample_rate = 44100 + sound = common_utils.get_whitenoise(sample_rate=sample_rate, duration=60) sound = sound.mean(dim=0, keepdim=True) spec_ta = F.spectrogram( sound, pad=0, window=torch.hann_window(n_fft), n_fft=n_fft, @@ -347,7 +344,3 @@ def test_InverseMelScale(self): # torch.dist(spec_lr, spec_ta, p=1) # >>> tensor(943.2759) assert torch.dist(spec_orig, spec_ta, p=1) < threshold - - -if __name__ == '__main__': - unittest.main() diff --git a/test/test_models.py b/test/test_models.py index c54a57cebd..10ca5c827b 100644 --- a/test/test_models.py +++ b/test/test_models.py @@ -74,7 +74,12 @@ def test_waveform(self): for upsample_scale in upsample_scales: total_scale *= upsample_scale - model = _UpsampleNetwork(upsample_scales, n_res_block, n_freq, n_hidden, n_output, kernel_size) + model = _UpsampleNetwork(upsample_scales, + n_res_block, + n_freq, + n_hidden, + n_output, + kernel_size) x = torch.rand(n_batch, n_freq, n_time) out1, out2 = model(x) @@ -86,14 +91,13 @@ def test_waveform(self): class TestWaveRNN(common_utils.TorchaudioTestCase): def test_waveform(self): - """Validate the output dimensions of a _WaveRNN model in waveform mode. + """Validate the output dimensions of a _WaveRNN model. """ upsample_scales = [5, 5, 8] n_rnn = 512 n_fc = 512 - n_bits = 9 - sample_rate = 24000 + n_classes = 512 hop_length = 200 n_batch = 2 n_time = 200 @@ -102,41 +106,12 @@ def test_waveform(self): n_res_block = 10 n_hidden = 128 kernel_size = 5 - mode = 'waveform' - model = _WaveRNN(upsample_scales, n_bits, sample_rate, hop_length, n_res_block, - n_rnn, n_fc, kernel_size, n_freq, n_hidden, n_output, mode) + model = _WaveRNN(upsample_scales, n_classes, hop_length, n_res_block, + n_rnn, n_fc, kernel_size, n_freq, n_hidden, n_output) x = torch.rand(n_batch, 1, hop_length * (n_time - kernel_size + 1)) mels = torch.rand(n_batch, 1, n_freq, n_time) out = model(x, mels) - assert out.size() == (n_batch, 1, hop_length * (n_time - kernel_size + 1), 2 ** n_bits) - - def test_mol(self): - """Validate the output dimensions of a _WaveRNN model in mol mode. - """ - - upsample_scales = [5, 5, 8] - n_rnn = 512 - n_fc = 512 - n_bits = 9 - sample_rate = 24000 - hop_length = 200 - n_batch = 2 - n_time = 200 - n_freq = 100 - n_output = 256 - n_res_block = 10 - n_hidden = 128 - kernel_size = 5 - mode = 'mol' - - model = _WaveRNN(upsample_scales, n_bits, sample_rate, hop_length, n_res_block, - n_rnn, n_fc, kernel_size, n_freq, n_hidden, n_output, mode) - - x = torch.rand(n_batch, 1, hop_length * (n_time - kernel_size + 1)) - mels = torch.rand(n_batch, 1, n_freq, n_time) - out = model(x, mels) - - assert out.size() == (n_batch, 1, hop_length * (n_time - kernel_size + 1), 30) + assert out.size() == (n_batch, 1, hop_length * (n_time - kernel_size + 1), n_classes) diff --git a/test/test_sox_compatibility.py b/test/test_sox_compatibility.py index a5d9f8633c..260c435b83 100644 --- a/test/test_sox_compatibility.py +++ b/test/test_sox_compatibility.py @@ -1,492 +1,296 @@ import unittest import torch -import torchaudio import torchaudio.functional as F import torchaudio.transforms as T - -from . import common_utils - - -@common_utils.skipIfNoSoxBackend -class TestFunctionalFiltering(common_utils.TempDirMixin, common_utils.TorchaudioTestCase): - backend = 'sox' - - def setUp(self): - # 1. Create int16 signal to save as PCM wav - # 2. Write to temp file - # 3. Load temp file into tensor to reuse in downstream tests - # Prefer to use common_utils.load_wav() but this implementation does - # not match torchaudio.load and errors on downstream tests - super().setUp() - - self.NOISE_SAMPLE_RATE = 44100 # N.B. 44.1 kHz required by SoX deemph effect - noise_waveform_as_int = common_utils.get_whitenoise( - sample_rate=self.NOISE_SAMPLE_RATE, duration=5, dtype=torch.int16, scale_factor=0.9, +from parameterized import parameterized + +from .common_utils import ( + skipIfNoSoxBackend, + skipIfNoExec, + TempDirMixin, + TorchaudioTestCase, + get_asset_path, + sox_utils, + load_wav, + save_wav, + get_whitenoise, +) + + +@skipIfNoSoxBackend +@skipIfNoExec('sox') +class TestFunctionalFiltering(TempDirMixin, TorchaudioTestCase): + def run_sox_effect(self, input_file, effect): + output_file = self.get_temp_path('expected.wav') + sox_utils.run_sox_effect(input_file, output_file, [str(e) for e in effect]) + return load_wav(output_file) + + def assert_sox_effect(self, result, input_path, effects, atol=1e-04, rtol=1e-5): + expected, _ = self.run_sox_effect(input_path, effects) + self.assertEqual(result, expected, atol=atol, rtol=rtol) + + def get_whitenoise(self, sample_rate=8000): + noise = get_whitenoise( + sample_rate=sample_rate, duration=3, scale_factor=0.9, ) - self.noise_filepath = self.get_temp_path("whitenoise.wav") - common_utils.save_wav( - self.noise_filepath, noise_waveform_as_int, self.NOISE_SAMPLE_RATE - ) - self.noise_waveform, _ = torchaudio.load(self.noise_filepath, normalization=True) + path = self.get_temp_path("whitenoise.wav") + save_wav(path, noise, sample_rate) + return noise, path def test_gain(self): - test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') - waveform, _ = common_utils.load_wav(test_filepath) - - waveform_gain = F.gain(waveform, 3) - self.assertTrue(waveform_gain.abs().max().item(), 1.) - - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(test_filepath) - E.append_effect_to_chain("gain", [3]) - sox_gain_waveform = E.sox_build_flow_effects()[0] - - self.assertEqual(waveform_gain, sox_gain_waveform, atol=1e-04, rtol=1e-5) + path = get_asset_path('steam-train-whistle-daniel_simon.wav') + data, _ = load_wav(path) + result = F.gain(data, 3) + self.assert_sox_effect(result, path, ['gain', 3]) def test_dither(self): - test_filepath = common_utils.get_asset_path('steam-train-whistle-daniel_simon.wav') - waveform, _ = common_utils.load_wav(test_filepath) - - waveform_dithered = F.dither(waveform) - waveform_dithered_noiseshaped = F.dither(waveform, noise_shaping=True) + path = get_asset_path('steam-train-whistle-daniel_simon.wav') + data, _ = load_wav(path) + result = F.dither(data) + self.assert_sox_effect(result, path, ['dither']) - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(test_filepath) - E.append_effect_to_chain("dither", []) - sox_dither_waveform = E.sox_build_flow_effects()[0] - - self.assertEqual(waveform_dithered, sox_dither_waveform, atol=1e-04, rtol=1e-5) - E.clear_chain() - - E.append_effect_to_chain("dither", ["-s"]) - sox_dither_waveform_ns = E.sox_build_flow_effects()[0] - - self.assertEqual(waveform_dithered_noiseshaped, sox_dither_waveform_ns, atol=1e-02, rtol=1e-5) - - def test_vctk_transform_pipeline(self): - test_filepath_vctk = common_utils.get_asset_path('VCTK-Corpus', 'wav48', 'p224', 'p224_002.wav') - wf_vctk, sr_vctk = common_utils.load_wav(test_filepath_vctk) - - # rate - sample = T.Resample(sr_vctk, 16000, resampling_method='sinc_interpolation') - wf_vctk = sample(wf_vctk) - # dither - wf_vctk = F.dither(wf_vctk, noise_shaping=True) - - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(test_filepath_vctk) - E.append_effect_to_chain("gain", ["-h"]) - E.append_effect_to_chain("channels", [1]) - E.append_effect_to_chain("rate", [16000]) - E.append_effect_to_chain("gain", ["-rh"]) - E.append_effect_to_chain("dither", ["-s"]) - wf_vctk_sox = E.sox_build_flow_effects()[0] - - self.assertEqual(wf_vctk, wf_vctk_sox, rtol=1e-03, atol=1e-03) + def test_dither_noise(self): + path = get_asset_path('steam-train-whistle-daniel_simon.wav') + data, _ = load_wav(path) + result = F.dither(data, noise_shaping=True) + self.assert_sox_effect(result, path, ['dither', '-s'], atol=1.5e-4) def test_lowpass(self): - """ - Test biquad lowpass filter, compare to SoX implementation - """ cutoff_freq = 3000 + sample_rate = 8000 - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.noise_filepath) - E.append_effect_to_chain("lowpass", [cutoff_freq]) - sox_output_waveform, sr = E.sox_build_flow_effects() - - output_waveform = F.lowpass_biquad(self.noise_waveform, self.NOISE_SAMPLE_RATE, cutoff_freq) - - self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + data, path = self.get_whitenoise(sample_rate) + result = F.lowpass_biquad(data, sample_rate, cutoff_freq) + self.assert_sox_effect(result, path, ['lowpass', cutoff_freq], atol=1.5e-4) def test_highpass(self): - """ - Test biquad highpass filter, compare to SoX implementation - """ cutoff_freq = 2000 + sample_rate = 8000 - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.noise_filepath) - E.append_effect_to_chain("highpass", [cutoff_freq]) - sox_output_waveform, sr = E.sox_build_flow_effects() - - output_waveform = F.highpass_biquad(self.noise_waveform, self.NOISE_SAMPLE_RATE, cutoff_freq) - - self.assertEqual(output_waveform, sox_output_waveform, atol=1.5e-3, rtol=1e-5) + data, path = self.get_whitenoise(sample_rate) + result = F.highpass_biquad(data, sample_rate, cutoff_freq) + self.assert_sox_effect(result, path, ['highpass', cutoff_freq], atol=1.5e-4) def test_allpass(self): - """ - Test biquad allpass filter, compare to SoX implementation - """ central_freq = 1000 q = 0.707 + sample_rate = 8000 - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.noise_filepath) - E.append_effect_to_chain("allpass", [central_freq, str(q) + 'q']) - sox_output_waveform, sr = E.sox_build_flow_effects() - - output_waveform = F.allpass_biquad(self.noise_waveform, self.NOISE_SAMPLE_RATE, central_freq, q) - - self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + data, path = self.get_whitenoise(sample_rate) + result = F.allpass_biquad(data, sample_rate, central_freq, q) + self.assert_sox_effect(result, path, ['allpass', central_freq, f'{q}q']) def test_bandpass_with_csg(self): - """ - Test biquad bandpass filter, compare to SoX implementation - """ central_freq = 1000 q = 0.707 const_skirt_gain = True + sample_rate = 8000 - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.noise_filepath) - E.append_effect_to_chain("bandpass", ["-c", central_freq, str(q) + 'q']) - sox_output_waveform, sr = E.sox_build_flow_effects() - - output_waveform = F.bandpass_biquad(self.noise_waveform, self.NOISE_SAMPLE_RATE, - central_freq, q, const_skirt_gain) - - self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + data, path = self.get_whitenoise(sample_rate) + result = F.bandpass_biquad(data, sample_rate, central_freq, q, const_skirt_gain) + self.assert_sox_effect(result, path, ['bandpass', '-c', central_freq, f'{q}q']) def test_bandpass_without_csg(self): - """ - Test biquad bandpass filter, compare to SoX implementation - """ central_freq = 1000 q = 0.707 const_skirt_gain = False + sample_rate = 8000 - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.noise_filepath) - E.append_effect_to_chain("bandpass", [central_freq, str(q) + 'q']) - sox_output_waveform, sr = E.sox_build_flow_effects() - - output_waveform = F.bandpass_biquad(self.noise_waveform, self.NOISE_SAMPLE_RATE, - central_freq, q, const_skirt_gain) - - self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + data, path = self.get_whitenoise(sample_rate) + result = F.bandpass_biquad(data, sample_rate, central_freq, q, const_skirt_gain) + self.assert_sox_effect(result, path, ['bandpass', central_freq, f'{q}q']) def test_bandreject(self): - """ - Test biquad bandreject filter, compare to SoX implementation - """ central_freq = 1000 q = 0.707 + sample_rate = 8000 - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.noise_filepath) - E.append_effect_to_chain("bandreject", [central_freq, str(q) + 'q']) - sox_output_waveform, sr = E.sox_build_flow_effects() - - output_waveform = F.bandreject_biquad(self.noise_waveform, self.NOISE_SAMPLE_RATE, - central_freq, q) - - self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + data, path = self.get_whitenoise(sample_rate) + result = F.bandreject_biquad(data, sample_rate, central_freq, q) + self.assert_sox_effect(result, path, ['bandreject', central_freq, f'{q}q']) def test_band_with_noise(self): - """ - Test biquad band filter with noise mode, compare to SoX implementation - """ central_freq = 1000 q = 0.707 noise = True + sample_rate = 8000 - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.noise_filepath) - E.append_effect_to_chain("band", ["-n", central_freq, str(q) + 'q']) - sox_output_waveform, sr = E.sox_build_flow_effects() - - output_waveform = F.band_biquad(self.noise_waveform, self.NOISE_SAMPLE_RATE, central_freq, q, noise) - - self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + data, path = self.get_whitenoise(sample_rate) + result = F.band_biquad(data, sample_rate, central_freq, q, noise) + self.assert_sox_effect(result, path, ['band', '-n', central_freq, f'{q}q']) def test_band_without_noise(self): - """ - Test biquad band filter without noise mode, compare to SoX implementation - """ - central_freq = 1000 q = 0.707 noise = False + sample_rate = 8000 - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.noise_filepath) - E.append_effect_to_chain("band", [central_freq, str(q) + 'q']) - sox_output_waveform, sr = E.sox_build_flow_effects() - - output_waveform = F.band_biquad(self.noise_waveform, self.NOISE_SAMPLE_RATE, central_freq, q, noise) - - self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + data, path = self.get_whitenoise(sample_rate) + result = F.band_biquad(data, sample_rate, central_freq, q, noise) + self.assert_sox_effect(result, path, ['band', central_freq, f'{q}q']) def test_treble(self): - """ - Test biquad treble filter, compare to SoX implementation - """ - central_freq = 1000 q = 0.707 gain = 40 + sample_rate = 8000 - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.noise_filepath) - E.append_effect_to_chain("treble", [gain, central_freq, str(q) + 'q']) - sox_output_waveform, sr = E.sox_build_flow_effects() - - output_waveform = F.treble_biquad(self.noise_waveform, self.NOISE_SAMPLE_RATE, gain, central_freq, q) - - self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + data, path = self.get_whitenoise(sample_rate) + result = F.treble_biquad(data, sample_rate, gain, central_freq, q) + self.assert_sox_effect(result, path, ['treble', gain, central_freq, f'{q}q']) def test_bass(self): - """ - Test biquad bass filter, compare to SoX implementation - """ - central_freq = 1000 q = 0.707 gain = 40 + sample_rate = 8000 - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.noise_filepath) - E.append_effect_to_chain("bass", [gain, central_freq, str(q) + 'q']) - sox_output_waveform, sr = E.sox_build_flow_effects() - - output_waveform = F.bass_biquad(self.noise_waveform, self.NOISE_SAMPLE_RATE, gain, central_freq, q) - - self.assertEqual(output_waveform, sox_output_waveform, atol=1.5e-4, rtol=1e-5) + data, path = self.get_whitenoise(sample_rate) + result = F.bass_biquad(data, sample_rate, gain, central_freq, q) + self.assert_sox_effect(result, path, ['bass', gain, central_freq, f'{q}q'], atol=1.5e-4) def test_deemph(self): - """ - Test biquad deemph filter, compare to SoX implementation - """ - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.noise_filepath) - E.append_effect_to_chain("deemph") - sox_output_waveform, sr = E.sox_build_flow_effects() - - output_waveform = F.deemph_biquad(self.noise_waveform, self.NOISE_SAMPLE_RATE) - - self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + sample_rate = 44100 + data, path = self.get_whitenoise(sample_rate) + result = F.deemph_biquad(data, sample_rate) + self.assert_sox_effect(result, path, ['deemph']) def test_riaa(self): - """ - Test biquad riaa filter, compare to SoX implementation - """ - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.noise_filepath) - E.append_effect_to_chain("riaa") - sox_output_waveform, sr = E.sox_build_flow_effects() - - output_waveform = F.riaa_biquad(self.noise_waveform, self.NOISE_SAMPLE_RATE) - - self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + sample_rate = 44100 + data, path = self.get_whitenoise(sample_rate) + result = F.riaa_biquad(data, sample_rate) + self.assert_sox_effect(result, path, ['riaa']) def test_contrast(self): - """ - Test contrast effect, compare to SoX implementation - """ enhancement_amount = 80. - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.noise_filepath) - E.append_effect_to_chain("contrast", [enhancement_amount]) - sox_output_waveform, sr = E.sox_build_flow_effects() - - output_waveform = F.contrast(self.noise_waveform, enhancement_amount) - - self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + data, path = self.get_whitenoise() + result = F.contrast(data, enhancement_amount) + self.assert_sox_effect(result, path, ['contrast', enhancement_amount]) def test_dcshift_with_limiter(self): - """ - Test dcshift effect, compare to SoX implementation - """ shift = 0.5 limiter_gain = 0.05 - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.noise_filepath) - E.append_effect_to_chain("dcshift", [shift, limiter_gain]) - sox_output_waveform, sr = E.sox_build_flow_effects() - - output_waveform = F.dcshift(self.noise_waveform, shift, limiter_gain) - - self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + data, path = self.get_whitenoise() + result = F.dcshift(data, shift, limiter_gain) + self.assert_sox_effect(result, path, ['dcshift', shift, limiter_gain]) def test_dcshift_without_limiter(self): - """ - Test dcshift effect, compare to SoX implementation - """ shift = 0.6 - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.noise_filepath) - E.append_effect_to_chain("dcshift", [shift]) - sox_output_waveform, sr = E.sox_build_flow_effects() - - output_waveform = F.dcshift(self.noise_waveform, shift) - - self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + data, path = self.get_whitenoise() + result = F.dcshift(data, shift) + self.assert_sox_effect(result, path, ['dcshift', shift]) def test_overdrive(self): - """ - Test overdrive effect, compare to SoX implementation - """ gain = 30 colour = 40 - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.noise_filepath) - E.append_effect_to_chain("overdrive", [gain, colour]) - sox_output_waveform, sr = E.sox_build_flow_effects() - - output_waveform = F.overdrive(self.noise_waveform, gain, colour) - - self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + data, path = self.get_whitenoise() + result = F.overdrive(data, gain, colour) + self.assert_sox_effect(result, path, ['overdrive', gain, colour]) def test_phaser_sine(self): - """ - Test phaser effect with sine moduldation, compare to SoX implementation - """ gain_in = 0.5 gain_out = 0.8 delay_ms = 2.0 decay = 0.4 speed = 0.5 + sample_rate = 8000 - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.noise_filepath) - E.append_effect_to_chain("phaser", [gain_in, gain_out, delay_ms, decay, speed, "-s"]) - sox_output_waveform, sr = E.sox_build_flow_effects() - - output_waveform = F.phaser(self.noise_waveform, self.NOISE_SAMPLE_RATE, - gain_in, gain_out, delay_ms, decay, speed, sinusoidal=True) - - self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + data, path = self.get_whitenoise(sample_rate) + result = F.phaser(data, sample_rate, gain_in, gain_out, delay_ms, decay, speed, sinusoidal=True) + self.assert_sox_effect(result, path, ['phaser', gain_in, gain_out, delay_ms, decay, speed, '-s']) def test_phaser_triangle(self): - """ - Test phaser effect with triangle modulation, compare to SoX implementation - """ gain_in = 0.5 gain_out = 0.8 delay_ms = 2.0 decay = 0.4 speed = 0.5 + sample_rate = 8000 - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.noise_filepath) - E.append_effect_to_chain("phaser", [gain_in, gain_out, delay_ms, decay, speed, "-t"]) - sox_output_waveform, sr = E.sox_build_flow_effects() - - output_waveform = F.phaser(self.noise_waveform, self.NOISE_SAMPLE_RATE, - gain_in, gain_out, delay_ms, decay, speed, sinusoidal=False) - - self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + data, path = self.get_whitenoise(sample_rate) + result = F.phaser(data, sample_rate, gain_in, gain_out, delay_ms, decay, speed, sinusoidal=False) + self.assert_sox_effect(result, path, ['phaser', gain_in, gain_out, delay_ms, decay, speed, '-t']) def test_flanger_triangle_linear(self): - """ - Test flanger effect with triangle modulation and linear interpolation, compare to SoX implementation - """ delay = 0.6 depth = 0.87 regen = 3.0 width = 0.9 speed = 0.5 phase = 30 + sample_rate = 8000 - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.noise_filepath) - E.append_effect_to_chain("flanger", [delay, depth, regen, width, speed, "triangle", phase, "linear"]) - sox_output_waveform, sr = E.sox_build_flow_effects() - - output_waveform = F.flanger(self.noise_waveform, self.NOISE_SAMPLE_RATE, delay, depth, regen, - width, speed, phase, modulation='triangular', interpolation='linear') - - torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + data, path = self.get_whitenoise(sample_rate) + result = F.flanger( + data, sample_rate, delay, depth, regen, width, speed, phase, + modulation='triangular', interpolation='linear') + self.assert_sox_effect( + result, path, ['flanger', delay, depth, regen, width, speed, 'triangle', phase, 'linear']) def test_flanger_triangle_quad(self): - """ - Test flanger effect with triangle modulation and quadratic interpolation, compare to SoX implementation - """ delay = 0.8 depth = 0.88 regen = 3.0 width = 0.4 speed = 0.5 phase = 40 + sample_rate = 8000 - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.noise_filepath) - E.append_effect_to_chain("flanger", [delay, depth, regen, width, speed, "triangle", phase, "quadratic"]) - sox_output_waveform, sr = E.sox_build_flow_effects() - - output_waveform = F.flanger(self.noise_waveform, self.NOISE_SAMPLE_RATE, delay, depth, - regen, width, speed, phase, modulation='triangular', interpolation='quadratic') - - torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + data, path = self.get_whitenoise(sample_rate) + result = F.flanger( + data, sample_rate, delay, depth, regen, width, speed, phase, + modulation='triangular', interpolation='quadratic') + self.assert_sox_effect( + result, path, ['flanger', delay, depth, regen, width, speed, 'triangle', phase, 'quadratic']) def test_flanger_sine_linear(self): - """ - Test flanger effect with sine modulation and linear interpolation, compare to SoX implementation - """ delay = 0.8 depth = 0.88 regen = 3.0 width = 0.23 speed = 1.3 phase = 60 + sample_rate = 8000 - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.noise_filepath) - E.append_effect_to_chain("flanger", [delay, depth, regen, width, speed, "sine", phase, "linear"]) - sox_output_waveform, sr = E.sox_build_flow_effects() - - output_waveform = F.flanger(self.noise_waveform, self.NOISE_SAMPLE_RATE, delay, depth, - regen, width, speed, phase, modulation='sinusoidal', interpolation='linear') - - torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + data, path = self.get_whitenoise(sample_rate) + result = F.flanger( + data, sample_rate, delay, depth, regen, width, speed, phase, + modulation='sinusoidal', interpolation='linear') + self.assert_sox_effect( + result, path, ['flanger', delay, depth, regen, width, speed, 'sine', phase, 'linear']) def test_flanger_sine_quad(self): - """ - Test flanger effect with sine modulation and quadratic interpolation, compare to SoX implementation - """ delay = 0.9 depth = 0.9 regen = 4.0 width = 0.23 speed = 1.3 phase = 25 + sample_rate = 8000 - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.noise_filepath) - E.append_effect_to_chain("flanger", [delay, depth, regen, width, speed, "sine", phase, "quadratic"]) - sox_output_waveform, sr = E.sox_build_flow_effects() - - output_waveform = F.flanger(self.noise_waveform, self.NOISE_SAMPLE_RATE, delay, depth, - regen, width, speed, phase, modulation='sinusoidal', interpolation='quadratic') - - torch.testing.assert_allclose(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + data, path = self.get_whitenoise(sample_rate) + result = F.flanger( + data, sample_rate, delay, depth, regen, width, speed, phase, + modulation='sinusoidal', interpolation='quadratic') + self.assert_sox_effect( + result, path, ['flanger', delay, depth, regen, width, speed, 'sine', phase, 'quadratic']) def test_equalizer(self): - """ - Test biquad peaking equalizer filter, compare to SoX implementation - """ - center_freq = 300 q = 0.707 gain = 1 + sample_rate = 8000 - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.noise_filepath) - E.append_effect_to_chain("equalizer", [center_freq, q, gain]) - sox_output_waveform, sr = E.sox_build_flow_effects() - - output_waveform = F.equalizer_biquad(self.noise_waveform, self.NOISE_SAMPLE_RATE, center_freq, gain, q) - - self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5) + data, path = self.get_whitenoise(sample_rate) + result = F.equalizer_biquad(data, sample_rate, center_freq, gain, q) + self.assert_sox_effect(result, path, ['equalizer', center_freq, q, gain]) def test_perf_biquad_filtering(self): - b0 = 0.4 b1 = 0.2 b2 = 0.9 @@ -494,18 +298,34 @@ def test_perf_biquad_filtering(self): a1 = 0.2 a2 = 0.6 - # SoX method - E = torchaudio.sox_effects.SoxEffectsChain() - E.set_input_file(self.noise_filepath) - E.append_effect_to_chain("biquad", [b0, b1, b2, a0, a1, a2]) - waveform_sox_out, _ = E.sox_build_flow_effects() - - waveform_lfilter_out = F.lfilter( - self.noise_waveform, torch.tensor([a0, a1, a2]), torch.tensor([b0, b1, b2]) - ) - - self.assertEqual(waveform_lfilter_out, waveform_sox_out, atol=1e-4, rtol=1e-5) - - -if __name__ == "__main__": - unittest.main() + data, path = self.get_whitenoise() + result = F.lfilter(data, torch.tensor([a0, a1, a2]), torch.tensor([b0, b1, b2])) + self.assert_sox_effect(result, path, ['biquad', b0, b1, b2, a0, a1, a2]) + + @parameterized.expand([ + ('q', 'quarter_sine'), + ('h', 'half_sine'), + ('t', 'linear'), + ]) + def test_fade(self, fade_shape_sox, fade_shape): + fade_in_len, fade_out_len = 44100, 44100 + data, path = self.get_whitenoise(sample_rate=44100) + result = T.Fade(fade_in_len, fade_out_len, fade_shape)(data) + self.assert_sox_effect(result, path, ['fade', fade_shape_sox, '1', '0', '1']) + + @parameterized.expand([ + ('amplitude', 1.1), + ('db', 2), + ('power', 2), + ]) + def test_vol(self, gain_type, gain): + data, path = self.get_whitenoise() + result = T.Vol(gain, gain_type)(data) + self.assert_sox_effect(result, path, ['vol', f'{gain}', gain_type]) + + @parameterized.expand(['vad-go-stereo-44100.wav', 'vad-go-mono-32000.wav']) + def test_vad(self, filename): + path = get_asset_path(filename) + data, sample_rate = load_wav(path) + result = T.Vad(sample_rate)(data) + self.assert_sox_effect(result, path, ['vad']) diff --git a/test/test_transforms.py b/test/test_transforms.py index 6df9562bc8..f6942e18a5 100644 --- a/test/test_transforms.py +++ b/test/test_transforms.py @@ -219,7 +219,3 @@ def test_compute_deltas_twochannel(self): computed = transform(specgram) assert computed.shape == expected.shape, (computed.shape, expected.shape) self.assertEqual(computed, expected, atol=1e-6, rtol=1e-8) - - -if __name__ == '__main__': - unittest.main() diff --git a/test/utils/__init__.py b/test/utils/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/test/utils/test_sox_utils.py b/test/utils/test_sox_utils.py new file mode 100644 index 0000000000..55a46a235e --- /dev/null +++ b/test/utils/test_sox_utils.py @@ -0,0 +1,49 @@ +from torchaudio.utils import sox_utils + +from ..common_utils import ( + PytorchTestCase, + skipIfNoExtension, +) + + +@skipIfNoExtension +class TestSoxUtils(PytorchTestCase): + """Smoke tests for sox_util module""" + def test_set_seed(self): + """`set_seed` does not crush""" + sox_utils.set_seed(0) + + def test_set_verbosity(self): + """`set_verbosity` does not crush""" + for val in range(6, 0, -1): + sox_utils.set_verbosity(val) + + def test_set_buffer_size(self): + """`set_buffer_size` does not crush""" + sox_utils.set_buffer_size(131072) + # back to default + sox_utils.set_buffer_size(8192) + + def test_set_use_threads(self): + """`set_use_threads` does not crush""" + sox_utils.set_use_threads(True) + # back to default + sox_utils.set_use_threads(False) + + def test_list_effects(self): + """`list_effects` returns the list of available effects""" + effects = sox_utils.list_effects() + # We cannot infer what effects are available, so only check some of them. + assert 'highpass' in effects + assert 'phaser' in effects + assert 'gain' in effects + + def test_list_read_formats(self): + """`list_read_formats` returns the list of supported formats""" + formats = sox_utils.list_read_formats() + assert 'wav' in formats + + def test_list_write_formats(self): + """`list_write_formats` returns the list of supported formats""" + formats = sox_utils.list_write_formats() + assert 'opus' not in formats diff --git a/torchaudio/__init__.py b/torchaudio/__init__.py index 16419ff3ff..9a6a5e3c16 100644 --- a/torchaudio/__init__.py +++ b/torchaudio/__init__.py @@ -4,6 +4,7 @@ compliance, datasets, kaldi_io, + utils, sox_effects, transforms ) @@ -34,10 +35,10 @@ @_mod_utils.deprecated( "Please remove the function call to initialize_sox. " "Resource initialization is now automatically handled.") -def initialize_sox() -> int: +def initialize_sox(): """Initialize sox effects. - This function is deprecated. See ``torchaudio.sox_effects.init_sox_effects`` + This function is deprecated. See :py:func:`torchaudio.sox_effects.init_sox_effects` """ _init_sox_effects() @@ -50,6 +51,6 @@ def initialize_sox() -> int: def shutdown_sox(): """Shutdown sox effects. - This function is deprecated. See ``torchaudio.sox_effects.shutdown_sox_effects`` + This function is deprecated. See :py:func:`torchaudio.sox_effects.shutdown_sox_effects` """ _shutdown_sox_effects() diff --git a/torchaudio/_internal/module_utils.py b/torchaudio/_internal/module_utils.py index 575a76c1ed..31bc2f1a31 100644 --- a/torchaudio/_internal/module_utils.py +++ b/torchaudio/_internal/module_utils.py @@ -48,7 +48,7 @@ def decorator(func): def wrapped(*args, **kwargs): message = ( f'{func.__module__}.{func.__name__} has been deprecated ' - f'and will be removed from {"future" if version is None else version} release.' + f'and will be removed from {"future" if version is None else version} release. ' f'{direction}') warnings.warn(message, stacklevel=2) return func(*args, **kwargs) diff --git a/torchaudio/backend/common.py b/torchaudio/backend/common.py index 0593c34bd5..bc61df4746 100644 --- a/torchaudio/backend/common.py +++ b/torchaudio/backend/common.py @@ -2,6 +2,18 @@ class SignalInfo: + """Data class returned ``info`` functions. + + Used by :ref:`sox backend` and :ref:`soundfile backend` + + See https://fossies.org/dox/sox-14.4.2/structsox__signalinfo__t.html + + :ivar Optional[int] channels: The number of channels + :ivar Optional[float] rate: Sampleing rate + :ivar Optional[int] precision: Bit depth + :ivar Optional[int] length: For :ref:`sox backend`, the number of samples. + (frames * channels). For :ref:`soundfile backend`, the number of frames. + """ def __init__(self, channels: Optional[int] = None, rate: Optional[float] = None, @@ -14,6 +26,20 @@ def __init__(self, class EncodingInfo: + """Data class returned ``info`` functions. + + Used by :ref:`sox backend` and :ref:`soundfile backend` + + See https://fossies.org/dox/sox-14.4.2/structsox__encodinginfo__t.html + + :ivar Optional[int] encoding: sox_encoding_t + :ivar Optional[int] bits_per_sample: bit depth + :ivar Optional[float] compression: Compression option + :ivar Any reverse_bytes: + :ivar Any reverse_nibbles: + :ivar Any reverse_bits: + :ivar Optional[bool] opposite_endian: + """ def __init__(self, encoding: Any = None, bits_per_sample: Optional[int] = None, diff --git a/torchaudio/backend/sox_io_backend.py b/torchaudio/backend/sox_io_backend.py index 4664f733c6..57d8db7723 100644 --- a/torchaudio/backend/sox_io_backend.py +++ b/torchaudio/backend/sox_io_backend.py @@ -7,6 +7,12 @@ class AudioMetaData: + """Data class to be returned by :py:func:`~torchaudio.backend.sox_io_backend.info`. + + :ivar int sample_rate: Sample rate + :ivar int num_frames: The number of frames + :ivar int num_channels: The number of channels + """ def __init__(self, sample_rate: int, num_frames: int, num_channels: int): self.sample_rate = sample_rate self.num_frames = num_frames @@ -15,7 +21,14 @@ def __init__(self, sample_rate: int, num_frames: int, num_channels: int): @_mod_utils.requires_module('torchaudio._torchaudio') def info(filepath: str) -> AudioMetaData: - """Get signal information of an audio file.""" + """Get signal information of an audio file. + + Args: + filepath (str): Path to audio file + + Returns: + AudioMetaData: meta data of the given audio. + """ sinfo = torch.ops.torchaudio.sox_io_get_info(filepath) return AudioMetaData(sinfo.get_sample_rate(), sinfo.get_num_frames(), sinfo.get_num_channels()) @@ -30,21 +43,28 @@ def load( ) -> Tuple[torch.Tensor, int]: """Load audio data from file. - This function can handle all the codecs that underlying libsox can handle, however note the - followings. - Note: - This function is tested on the following formats; - - WAV - - 32-bit floating-point - - 32-bit signed integer - - 16-bit signed integer - - 8-bit unsigned integer - - MP3 - - FLAC - - OGG/VORBIS - - By default, this function returns Tensor with ``float32`` dtype and the shape of ``[channel, time]``. + This function can handle all the codecs that underlying libsox can handle, + however it is tested on the following formats; + + * WAV + + * 32-bit floating-point + * 32-bit signed integer + * 16-bit signed integer + * 8-bit unsigned integer + + * MP3 + * FLAC + * OGG/VORBIS + * OPUS + + To load ``MP3``, ``FLAC``, ``OGG/VORBIS``, ``OPUS`` and other codecs ``libsox`` does not + handle natively, your installation of ``torchaudio`` has to be linked to ``libsox`` + and corresponding codec libraries such as ``libmad`` or ``libmp3lame`` etc. + + By default (``normalize=True``, ``channels_first=True``), this function returns Tensor with + ``float32`` dtype and the shape of ``[channel, time]``. The samples are normalized to fit in the range of ``[-1.0, 1.0]``. When the input format is WAV with integer type, such as 32-bit signed integer, 16-bit @@ -54,24 +74,33 @@ def load( for 32-bit signed PCM, ``int16`` for 16-bit signed PCM and ``uint8`` for 8-bit unsigned PCM. ``normalize`` parameter has no effect on 32-bit floating-point WAV and other formats, such as - flac and mp3. For these formats, this function always returns ``float32`` Tensor with values - normalized to ``[-1.0, 1.0]``. + ``flac`` and ``mp3``. + For these formats, this function always returns ``float32`` Tensor with values normalized to + ``[-1.0, 1.0]``. Args: - filepath: Path to audio file - frame_offset: Number of frames to skip before start reading data. - num_frames: Maximum number of frames to read. -1 reads all the remaining samples, starting - from ``frame_offset``. This function may return the less number of frames if there is - not enough frames in the given file. - normalize: When ``True``, this function always return ``float32``, and sample values are - normalized to ``[-1.0, 1.0]``. If input file is integer WAV, giving ``False`` will change - the resulting Tensor type to integer type. This argument has no effect for formats other - than integer WAV type. - channels_first: When True, the returned Tensor has dimension ``[channel, time]``. + filepath (str): + Path to audio file + frame_offset (int): + Number of frames to skip before start reading data. + num_frames (int): + Maximum number of frames to read. ``-1`` reads all the remaining samples, + starting from ``frame_offset``. + This function may return the less number of frames if there is not enough + frames in the given file. + normalize (bool): + When ``True``, this function always return ``float32``, and sample values are + normalized to ``[-1.0, 1.0]``. + If input file is integer WAV, giving ``False`` will change the resulting Tensor type to + integer type. + This argument has no effect for formats other than integer WAV type. + channels_first (bool): + When True, the returned Tensor has dimension ``[channel, time]``. Otherwise, the returned Tensor's dimension is ``[time, channel]``. Returns: - torch.Tensor: If the input file has integer wav format and normalization is off, then it has + torch.Tensor: + If the input file has integer wav format and normalization is off, then it has integer type, else ``float32`` type. If ``channels_first=True``, it has ``[channel, time]`` else ``[time, channel]``. """ @@ -83,37 +112,49 @@ def load( @_mod_utils.requires_module('torchaudio._torchaudio') def save( filepath: str, - tensor: torch.Tensor, + src: torch.Tensor, sample_rate: int, channels_first: bool = True, compression: Optional[float] = None, ): """Save audio data to file. - Supported formats are; - - WAV - - 32-bit floating-point - - 32-bit signed integer - - 16-bit signed integer - - 8-bit unsigned integer - - MP3 - - FLAC - - OGG/VORBIS + Note: + Supported formats are; + + * WAV + + * 32-bit floating-point + * 32-bit signed integer + * 16-bit signed integer + * 8-bit unsigned integer + + * MP3 + * FLAC + * OGG/VORBIS + + To save ``MP3``, ``FLAC``, ``OGG/VORBIS``, and other codecs ``libsox`` does not + handle natively, your installation of ``torchaudio`` has to be linked to ``libsox`` + and corresponding codec libraries such as ``libmad`` or ``libmp3lame`` etc. Args: - filepath: Path to save file. - tensor: Audio data to save. must be 2D tensor. - sample_rate: sampling rate - channels_first: If True, the given tensor is interpreted as ``[channel, time]``. - compression: Used for formats other than WAV. This corresponds to ``-C`` option - of ``sox`` command. + filepath (str): Path to save file. + tensor (torch.Tensor): Audio data to save. must be 2D tensor. + sample_rate (int): sampling rate + channels_first (bool): + If ``True``, the given tensor is interpreted as ``[channel, time]``, + otherwise ``[time, channel]``. + compression (Optional[float]): + Used for formats other than WAV. This corresponds to ``-C`` option of ``sox`` command. + + * | ``MP3``: Either bitrate (in ``kbps``) with quality factor, such as ``128.2``, or + | VBR encoding with quality factor such as ``-4.2``. Default: ``-4.5``. + * | ``FLAC``: compression level. Whole number from ``0`` to ``8``. + | ``8`` is default and highest compression. + * | ``OGG/VORBIS``: number from ``-1`` to ``10``; ``-1`` is the highest compression + | and lowest quality. Default: ``3``. + See the detail at http://sox.sourceforge.net/soxformat.html. - - MP3: Either bitrate [kbps] with quality factor, such as ``128.2`` or - VBR encoding with quality factor such as ``-4.2``. Default: ``-4.5`` - - FLAC: compression level. Whole number from ``0`` to ``8``. - ``8`` is default and highest compression. - - OGG/VORBIS: number from -1 to 10; -1 is the highest compression and lowest - quality. Default: ``3``. """ if compression is None: ext = str(filepath)[-3:].lower() @@ -127,8 +168,22 @@ def save( compression = 3. else: raise RuntimeError(f'Unsupported file type: "{ext}"') - signal = torch.classes.torchaudio.TensorSignal(tensor, sample_rate, channels_first) + signal = torch.classes.torchaudio.TensorSignal(src, sample_rate, channels_first) torch.ops.torchaudio.sox_io_save_audio_file(filepath, signal, compression) -load_wav = load +@_mod_utils.requires_module('torchaudio._torchaudio') +def load_wav( + filepath: str, + frame_offset: int = 0, + num_frames: int = -1, + channels_first: bool = True, +) -> Tuple[torch.Tensor, int]: + """Load wave file. + + + This function is defined only for the purpose of compatibility against other backend + for simple usecases, such as ``torchaudio.load_wav(filepath)``. + The implementation is same as :py:func:`load`. + """ + return load(filepath, frame_offset, num_frames, normalize=False, channels_first=channels_first) diff --git a/torchaudio/backend/utils.py b/torchaudio/backend/utils.py index cb53b3e02f..84ec2fa3f5 100644 --- a/torchaudio/backend/utils.py +++ b/torchaudio/backend/utils.py @@ -19,7 +19,11 @@ def list_audio_backends() -> List[str]: - """List available backends""" + """List available backends + + Returns: + List[str]: The list of available backends. + """ backends = [] if is_module_available('soundfile'): backends.append('soundfile') @@ -29,12 +33,13 @@ def list_audio_backends() -> List[str]: return backends -def set_audio_backend(backend: Optional[str]) -> None: +def set_audio_backend(backend: Optional[str]): """Set the backend for I/O operation Args: - backend (str): Name of the backend. One of "sox" or "soundfile", - based on availability of the system. + backend (Optional[str]): Name of the backend. + One of ``"sox"``, ``"sox_io"`` or ``"soundfile"`` based on availability + of the system. If ``None`` is provided the current backend is unassigned. """ if backend is not None and backend not in list_audio_backends(): raise RuntimeError( @@ -68,7 +73,11 @@ def _init_audio_backend(): def get_audio_backend() -> Optional[str]: - """Get the name of the current backend""" + """Get the name of the current backend + + Returns: + Optional[str]: The name of the current backend or ``None`` if no backend is assigned. + """ if torchaudio.load == no_backend.load: return None if torchaudio.load == sox_backend.load: diff --git a/torchaudio/csrc/register.cpp b/torchaudio/csrc/register.cpp index 3c03232941..476dcc3ebb 100644 --- a/torchaudio/csrc/register.cpp +++ b/torchaudio/csrc/register.cpp @@ -18,6 +18,20 @@ static auto registerTensorSignal = .def("get_sample_rate", &sox_utils::TensorSignal::getSampleRate) .def("get_channels_first", &sox_utils::TensorSignal::getChannelsFirst); +static auto registerSetSoxOptions = + torch::RegisterOperators() + .op("torchaudio::sox_utils_set_seed", &sox_utils::set_seed) + .op("torchaudio::sox_utils_set_verbosity", &sox_utils::set_verbosity) + .op("torchaudio::sox_utils_set_use_threads", + &sox_utils::set_use_threads) + .op("torchaudio::sox_utils_set_buffer_size", + &sox_utils::set_buffer_size) + .op("torchaudio::sox_utils_list_effects", &sox_utils::list_effects) + .op("torchaudio::sox_utils_list_read_formats", + &sox_utils::list_read_formats) + .op("torchaudio::sox_utils_list_write_formats", + &sox_utils::list_write_formats); + //////////////////////////////////////////////////////////////////////////////// // sox_io.h //////////////////////////////////////////////////////////////////////////////// @@ -53,12 +67,23 @@ static auto registerSaveAudioFile = torch::RegisterOperators().op( // sox_effects.h //////////////////////////////////////////////////////////////////////////////// static auto registerSoxEffects = - torch::RegisterOperators( - "torchaudio::sox_effects_initialize_sox_effects", - &sox_effects::initialize_sox_effects) + torch::RegisterOperators() + .op("torchaudio::sox_effects_initialize_sox_effects", + &sox_effects::initialize_sox_effects) .op("torchaudio::sox_effects_shutdown_sox_effects", &sox_effects::shutdown_sox_effects) - .op("torchaudio::sox_effects_list_effects", &sox_effects::list_effects); + .op(torch::RegisterOperators::options() + .schema( + "torchaudio::sox_effects_apply_effects_tensor(__torch__.torch.classes.torchaudio.TensorSignal input_signal, str[][] effects) -> __torch__.torch.classes.torchaudio.TensorSignal output_signal") + .catchAllKernel< + decltype(sox_effects::apply_effects_tensor), + &sox_effects::apply_effects_tensor>()) + .op(torch::RegisterOperators::options() + .schema( + "torchaudio::sox_effects_apply_effects_file(str path, str[][] effects, bool normalize, bool channels_first) -> __torch__.torch.classes.torchaudio.TensorSignal output_signal") + .catchAllKernel< + decltype(sox_effects::apply_effects_file), + &sox_effects::apply_effects_file>()); } // namespace } // namespace torchaudio diff --git a/torchaudio/csrc/sox_effects.cpp b/torchaudio/csrc/sox_effects.cpp index 9a0c2ddc6f..8a7cc7c494 100644 --- a/torchaudio/csrc/sox_effects.cpp +++ b/torchaudio/csrc/sox_effects.cpp @@ -1,7 +1,9 @@ #include #include +#include +#include -using namespace torch::indexing; +using namespace torchaudio::sox_utils; namespace torchaudio { namespace sox_effects { @@ -10,44 +12,125 @@ namespace { enum SoxEffectsResourceState { NotInitialized, Initialized, ShutDown }; SoxEffectsResourceState SOX_RESOURCE_STATE = NotInitialized; +std::mutex SOX_RESOUCE_STATE_MUTEX; } // namespace void initialize_sox_effects() { - if (SOX_RESOURCE_STATE == ShutDown) { - throw std::runtime_error( - "SoX Effects has been shut down. Cannot initialize again."); - } - if (SOX_RESOURCE_STATE == NotInitialized) { - if (sox_init() != SOX_SUCCESS) { - throw std::runtime_error("Failed to initialize sox effects."); - }; - SOX_RESOURCE_STATE = Initialized; + const std::lock_guard lock(SOX_RESOUCE_STATE_MUTEX); + + switch (SOX_RESOURCE_STATE) { + case NotInitialized: + if (sox_init() != SOX_SUCCESS) { + throw std::runtime_error("Failed to initialize sox effects."); + }; + SOX_RESOURCE_STATE = Initialized; + case Initialized: + break; + case ShutDown: + throw std::runtime_error( + "SoX Effects has been shut down. Cannot initialize again."); } }; void shutdown_sox_effects() { - if (SOX_RESOURCE_STATE == NotInitialized) { - throw std::runtime_error( - "SoX Effects is not initialized. Cannot shutdown."); + const std::lock_guard lock(SOX_RESOUCE_STATE_MUTEX); + + switch (SOX_RESOURCE_STATE) { + case NotInitialized: + throw std::runtime_error( + "SoX Effects is not initialized. Cannot shutdown."); + case Initialized: + if (sox_quit() != SOX_SUCCESS) { + throw std::runtime_error("Failed to initialize sox effects."); + }; + SOX_RESOURCE_STATE = ShutDown; + case ShutDown: + break; } - if (SOX_RESOURCE_STATE == Initialized) { - if (sox_quit() != SOX_SUCCESS) { - throw std::runtime_error("Failed to initialize sox effects."); - }; - SOX_RESOURCE_STATE = ShutDown; +} + +c10::intrusive_ptr apply_effects_tensor( + const c10::intrusive_ptr& input_signal, + std::vector> effects) { + auto in_tensor = input_signal->getTensor(); + validate_input_tensor(in_tensor); + + // Create SoxEffectsChain + const auto dtype = in_tensor.dtype(); + torchaudio::sox_effects_chain::SoxEffectsChain chain( + /*input_encoding=*/get_encodinginfo("wav", dtype, 0.), + /*output_encoding=*/get_encodinginfo("wav", dtype, 0.)); + + // Prepare output buffer + std::vector out_buffer; + out_buffer.reserve(in_tensor.numel()); + + // Build and run effects chain + chain.addInputTensor(input_signal.get()); + for (const auto& effect : effects) { + chain.addEffect(effect); } + chain.addOutputBuffer(&out_buffer); + chain.run(); + + // Create tensor from buffer + const auto channels_first = input_signal->getChannelsFirst(); + auto out_tensor = convert_to_tensor( + /*buffer=*/out_buffer.data(), + /*num_samples=*/out_buffer.size(), + /*num_channels=*/chain.getOutputNumChannels(), + dtype, + /*noramlize=*/false, + channels_first); + + return c10::make_intrusive( + out_tensor, chain.getOutputSampleRate(), channels_first); } -std::vector list_effects() { - std::vector names; - const sox_effect_fn_t* fns = sox_get_effect_fns(); - for (int i = 0; fns[i]; ++i) { - const sox_effect_handler_t* handler = fns[i](); - if (handler && handler->name) - names.push_back(handler->name); +c10::intrusive_ptr apply_effects_file( + const std::string path, + std::vector> effects, + const bool normalize, + const bool channels_first) { + // Open input file + SoxFormat sf(sox_open_read( + path.c_str(), + /*signal=*/nullptr, + /*encoding=*/nullptr, + /*filetype=*/nullptr)); + + validate_input_file(sf); + + const auto dtype = get_dtype(sf->encoding.encoding, sf->signal.precision); + + // Prepare output + std::vector out_buffer; + out_buffer.reserve(sf->signal.length); + + // Create and run SoxEffectsChain + torchaudio::sox_effects_chain::SoxEffectsChain chain( + /*input_encoding=*/sf->encoding, + /*output_encoding=*/get_encodinginfo("wav", dtype, 0.)); + + chain.addInputFile(sf); + for (const auto& effect : effects) { + chain.addEffect(effect); } - return names; + chain.addOutputBuffer(&out_buffer); + chain.run(); + + // Create tensor from buffer + auto tensor = convert_to_tensor( + /*buffer=*/out_buffer.data(), + /*num_samples=*/out_buffer.size(), + /*num_channels=*/chain.getOutputNumChannels(), + dtype, + normalize, + channels_first); + + return c10::make_intrusive( + tensor, chain.getOutputSampleRate(), channels_first); } } // namespace sox_effects diff --git a/torchaudio/csrc/sox_effects.h b/torchaudio/csrc/sox_effects.h index 14bdbbfabc..7883a7beac 100644 --- a/torchaudio/csrc/sox_effects.h +++ b/torchaudio/csrc/sox_effects.h @@ -2,6 +2,7 @@ #define TORCHAUDIO_SOX_EFFECTS_H #include +#include namespace torchaudio { namespace sox_effects { @@ -10,7 +11,15 @@ void initialize_sox_effects(); void shutdown_sox_effects(); -std::vector list_effects(); +c10::intrusive_ptr apply_effects_tensor( + const c10::intrusive_ptr& input_signal, + std::vector> effects); + +c10::intrusive_ptr apply_effects_file( + const std::string path, + std::vector> effects, + const bool normalize = true, + const bool channels_first = true); } // namespace sox_effects } // namespace torchaudio diff --git a/torchaudio/csrc/sox_effects_chain.cpp b/torchaudio/csrc/sox_effects_chain.cpp new file mode 100644 index 0000000000..449e33be98 --- /dev/null +++ b/torchaudio/csrc/sox_effects_chain.cpp @@ -0,0 +1,287 @@ +#include +#include + +using namespace torch::indexing; +using namespace torchaudio::sox_utils; + +namespace torchaudio { +namespace sox_effects_chain { + +namespace { + +// Helper struct to safely close sox_effect_t* pointer returned by +// sox_create_effect +struct SoxEffect { + explicit SoxEffect(sox_effect_t* se) noexcept : se_(se){}; + SoxEffect(const SoxEffect& other) = delete; + SoxEffect(const SoxEffect&& other) = delete; + SoxEffect& operator=(const SoxEffect& other) = delete; + SoxEffect& operator=(SoxEffect&& other) = delete; + ~SoxEffect() { + if (se_ != nullptr) { + free(se_); + } + } + operator sox_effect_t*() const { + return se_; + }; + sox_effect_t* operator->() noexcept { + return se_; + } + + private: + sox_effect_t* se_; +}; + +/// helper classes for passing the location of input tensor and output buffer +/// +/// drain/flow callback functions require plaing C style function signature and +/// the way to pass extra data is to attach data to sox_fffect_t::priv pointer. +/// The following structs will be assigned to sox_fffect_t::priv pointer which +/// gives sox_effect_t an access to input Tensor and output buffer object. +struct TensorInputPriv { + size_t index; + TensorSignal* signal; +}; +struct TensorOutputPriv { + std::vector* buffer; +}; +struct FileOutputPriv { + sox_format_t* sf; +}; + +/// Callback function to feed Tensor data to SoxEffectChain. +int tensor_input_drain(sox_effect_t* effp, sox_sample_t* obuf, size_t* osamp) { + // Retrieve the input Tensor and current index + auto priv = static_cast(effp->priv); + auto index = priv->index; + auto signal = priv->signal; + auto tensor = signal->getTensor(); + auto num_channels = effp->out_signal.channels; + + // Adjust the number of samples to read + const size_t num_samples = tensor.numel(); + if (index + *osamp > num_samples) { + *osamp = num_samples - index; + } + // Ensure that it's a multiple of the number of channels + *osamp -= *osamp % num_channels; + + // Slice the input Tensor and unnormalize the values + const auto tensor_ = [&]() { + auto i_frame = index / num_channels; + auto num_frames = *osamp / num_channels; + auto t = (signal->getChannelsFirst()) + ? tensor.index({Slice(), Slice(i_frame, i_frame + num_frames)}).t() + : tensor.index({Slice(i_frame, i_frame + num_frames), Slice()}); + return unnormalize_wav(t.reshape({-1})).contiguous(); + }(); + priv->index += *osamp; + + // Write data to SoxEffectsChain buffer. + auto ptr = tensor_.data_ptr(); + std::copy(ptr, ptr + *osamp, obuf); + + return (priv->index == num_samples) ? SOX_EOF : SOX_SUCCESS; +} + +/// Callback function to fetch data from SoxEffectChain. +int tensor_output_flow( + sox_effect_t* effp, + sox_sample_t const* ibuf, + sox_sample_t* obuf LSX_UNUSED, + size_t* isamp, + size_t* osamp) { + *osamp = 0; + // Get output buffer + auto out_buffer = static_cast(effp->priv)->buffer; + // Append at the end + out_buffer->insert(out_buffer->end(), ibuf, ibuf + *isamp); + return SOX_SUCCESS; +} + +int file_output_flow( + sox_effect_t* effp, + sox_sample_t const* ibuf, + sox_sample_t* obuf LSX_UNUSED, + size_t* isamp, + size_t* osamp) { + *osamp = 0; + if (*isamp) { + auto sf = static_cast(effp->priv)->sf; + if (sox_write(sf, ibuf, *isamp) != *isamp) { + if (sf->sox_errno) { + std::ostringstream stream; + stream << sf->sox_errstr << " " << sox_strerror(sf->sox_errno) << " " + << sf->filename; + throw std::runtime_error(stream.str()); + } + return SOX_EOF; + } + } + return SOX_SUCCESS; +} + +sox_effect_handler_t* get_tensor_input_handler() { + static sox_effect_handler_t handler{/*name=*/"input_tensor", + /*usage=*/NULL, + /*flags=*/SOX_EFF_MCHAN, + /*getopts=*/NULL, + /*start=*/NULL, + /*flow=*/NULL, + /*drain=*/tensor_input_drain, + /*stop=*/NULL, + /*kill=*/NULL, + /*priv_size=*/sizeof(TensorInputPriv)}; + return &handler; +} + +sox_effect_handler_t* get_tensor_output_handler() { + static sox_effect_handler_t handler{/*name=*/"output_tensor", + /*usage=*/NULL, + /*flags=*/SOX_EFF_MCHAN, + /*getopts=*/NULL, + /*start=*/NULL, + /*flow=*/tensor_output_flow, + /*drain=*/NULL, + /*stop=*/NULL, + /*kill=*/NULL, + /*priv_size=*/sizeof(TensorOutputPriv)}; + return &handler; +} + +sox_effect_handler_t* get_file_output_handler() { + static sox_effect_handler_t handler{/*name=*/"output_file", + /*usage=*/NULL, + /*flags=*/SOX_EFF_MCHAN, + /*getopts=*/NULL, + /*start=*/NULL, + /*flow=*/file_output_flow, + /*drain=*/NULL, + /*stop=*/NULL, + /*kill=*/NULL, + /*priv_size=*/sizeof(FileOutputPriv)}; + return &handler; +} + +} // namespace + +SoxEffectsChain::SoxEffectsChain( + sox_encodinginfo_t input_encoding, + sox_encodinginfo_t output_encoding) + : in_enc_(input_encoding), + out_enc_(output_encoding), + in_sig_(), + interm_sig_(), + out_sig_(), + sec_(sox_create_effects_chain(&in_enc_, &out_enc_)) { + if (!sec_) { + throw std::runtime_error("Failed to create effect chain."); + } +} + +SoxEffectsChain::~SoxEffectsChain() { + if (sec_ != nullptr) { + sox_delete_effects_chain(sec_); + } +} + +void SoxEffectsChain::run() { + sox_flow_effects(sec_, NULL, NULL); +} + +void SoxEffectsChain::addInputTensor(TensorSignal* signal) { + in_sig_ = get_signalinfo(signal, "wav"); + interm_sig_ = in_sig_; + SoxEffect e(sox_create_effect(get_tensor_input_handler())); + auto priv = static_cast(e->priv); + priv->signal = signal; + priv->index = 0; + if (sox_add_effect(sec_, e, &interm_sig_, &in_sig_) != SOX_SUCCESS) { + throw std::runtime_error("Failed to add effect: input_tensor"); + } +} + +void SoxEffectsChain::addOutputBuffer( + std::vector* output_buffer) { + SoxEffect e(sox_create_effect(get_tensor_output_handler())); + static_cast(e->priv)->buffer = output_buffer; + if (sox_add_effect(sec_, e, &interm_sig_, &in_sig_) != SOX_SUCCESS) { + throw std::runtime_error("Failed to add effect: output_tensor"); + } +} + +void SoxEffectsChain::addInputFile(sox_format_t* sf) { + in_sig_ = sf->signal; + interm_sig_ = in_sig_; + SoxEffect e(sox_create_effect(sox_find_effect("input"))); + char* opts[] = {(char*)sf}; + sox_effect_options(e, 1, opts); + if (sox_add_effect(sec_, e, &interm_sig_, &in_sig_) != SOX_SUCCESS) { + std::ostringstream stream; + stream << "Failed to add effect: input " << sf->filename; + throw std::runtime_error(stream.str()); + } +} + +void SoxEffectsChain::addOutputFile(sox_format_t* sf) { + out_sig_ = sf->signal; + SoxEffect e(sox_create_effect(get_file_output_handler())); + static_cast(e->priv)->sf = sf; + if (sox_add_effect(sec_, e, &interm_sig_, &out_sig_) != SOX_SUCCESS) { + std::ostringstream stream; + stream << "Failed to add effect: output " << sf->filename; + throw std::runtime_error(stream.str()); + } +} + +void SoxEffectsChain::addEffect(const std::vector effect) { + const auto num_args = effect.size(); + if (num_args == 0) { + throw std::runtime_error("Invalid argument: empty effect."); + } + const auto name = effect[0]; + if (UNSUPPORTED_EFFECTS.find(name) != UNSUPPORTED_EFFECTS.end()) { + std::ostringstream stream; + stream << "Unsupported effect: " << name; + throw std::runtime_error(stream.str()); + } + + SoxEffect e(sox_create_effect(sox_find_effect(name.c_str()))); + const auto num_options = num_args - 1; + + std::vector opts; + for (size_t i = 1; i < num_args; ++i) { + opts.push_back((char*)effect[i].c_str()); + } + if (sox_effect_options(e, num_options, num_options ? opts.data() : nullptr) != + SOX_SUCCESS) { + std::ostringstream stream; + stream << "Invalid effect option:"; + for (const auto& v : effect) { + stream << " " << v; + } + throw std::runtime_error(stream.str()); + } + + if (sox_add_effect(sec_, e, &interm_sig_, &in_sig_) != SOX_SUCCESS) { + std::ostringstream stream; + stream << "Failed to add effect: \"" << name; + for (size_t i = 1; i < num_args; ++i) { + stream << " " << effect[i]; + } + stream << "\""; + throw std::runtime_error(stream.str()); + } +} + +int64_t SoxEffectsChain::getOutputNumChannels() { + return interm_sig_.channels; +} + +int64_t SoxEffectsChain::getOutputSampleRate() { + return interm_sig_.rate; +} + +} // namespace sox_effects_chain +} // namespace torchaudio diff --git a/torchaudio/csrc/sox_effects_chain.h b/torchaudio/csrc/sox_effects_chain.h new file mode 100644 index 0000000000..fa55182281 --- /dev/null +++ b/torchaudio/csrc/sox_effects_chain.h @@ -0,0 +1,42 @@ +#ifndef TORCHAUDIO_SOX_EFFECTS_CHAIN_H +#define TORCHAUDIO_SOX_EFFECTS_CHAIN_H + +#include +#include +#include + +namespace torchaudio { +namespace sox_effects_chain { + +// Helper struct to safely close sox_effects_chain_t with handy methods +class SoxEffectsChain { + const sox_encodinginfo_t in_enc_; + const sox_encodinginfo_t out_enc_; + sox_signalinfo_t in_sig_; + sox_signalinfo_t interm_sig_; + sox_signalinfo_t out_sig_; + sox_effects_chain_t* sec_; + + public: + explicit SoxEffectsChain( + sox_encodinginfo_t input_encoding, + sox_encodinginfo_t output_encoding); + SoxEffectsChain(const SoxEffectsChain& other) = delete; + SoxEffectsChain(const SoxEffectsChain&& other) = delete; + SoxEffectsChain& operator=(const SoxEffectsChain& other) = delete; + SoxEffectsChain& operator=(SoxEffectsChain&& other) = delete; + ~SoxEffectsChain(); + void run(); + void addInputTensor(torchaudio::sox_utils::TensorSignal* signal); + void addInputFile(sox_format_t* sf); + void addOutputBuffer(std::vector* output_buffer); + void addOutputFile(sox_format_t* sf); + void addEffect(const std::vector effect); + int64_t getOutputNumChannels(); + int64_t getOutputSampleRate(); +}; + +} // namespace sox_effects_chain +} // namespace torchaudio + +#endif diff --git a/torchaudio/csrc/sox_io.cpp b/torchaudio/csrc/sox_io.cpp index 5d308027bb..4092d215d0 100644 --- a/torchaudio/csrc/sox_io.cpp +++ b/torchaudio/csrc/sox_io.cpp @@ -1,4 +1,6 @@ #include +#include +#include #include #include @@ -60,64 +62,21 @@ c10::intrusive_ptr load_audio_file( "Invalid argument: num_frames must be -1 or greater than 0."); } - SoxFormat sf(sox_open_read( - path.c_str(), - /*signal=*/nullptr, - /*encoding=*/nullptr, - /*filetype=*/nullptr)); - - validate_input_file(sf); - - const int64_t num_channels = sf->signal.channels; - const int64_t num_total_samples = sf->signal.length; - const int64_t sample_start = sf->signal.channels * frame_offset; - - if (sox_seek(sf, sample_start, 0) == SOX_EOF) { - throw std::runtime_error("Error reading audio file: offset past EOF."); + std::vector> effects; + if (num_frames != -1) { + std::ostringstream offset, frames; + offset << frame_offset << "s"; + frames << "+" << num_frames << "s"; + effects.emplace_back( + std::vector{"trim", offset.str(), frames.str()}); + } else if (frame_offset != 0) { + std::ostringstream offset; + offset << frame_offset << "s"; + effects.emplace_back(std::vector{"trim", offset.str()}); } - const int64_t sample_end = [&]() { - if (num_frames == -1) - return num_total_samples; - const int64_t sample_end_ = num_channels * num_frames + sample_start; - if (num_total_samples < sample_end_) { - // For lossy encoding, it is difficult to predict exact size of buffer for - // reading the number of samples required. - // So we allocate buffer size of given `num_frames` and ask sox to read as - // much as possible. For lossless format, sox reads exact number of - // samples, but for lossy encoding, sox can end up reading less. (i.e. - // mp3) For the consistent behavior specification between lossy/lossless - // format, we allow users to provide `num_frames` value that exceeds #of - // available samples, and we adjust it here. - return num_total_samples; - } - return sample_end_; - }(); - - const int64_t max_samples = sample_end - sample_start; - - // Read samples into buffer - std::vector buffer; - buffer.reserve(max_samples); - const int64_t num_samples = sox_read(sf, buffer.data(), max_samples); - if (num_samples == 0) { - throw std::runtime_error( - "Error reading audio file: empty file or read operation failed."); - } - // NOTE: num_samples may be smaller than max_samples if the input - // format is compressed (i.e. mp3). - - // Convert to Tensor - auto tensor = convert_to_tensor( - buffer.data(), - num_samples, - num_channels, - get_dtype(sf->encoding.encoding, sf->signal.precision), - normalize, - channels_first); - - return c10::make_intrusive( - tensor, static_cast(sf->signal.rate), channels_first); + return torchaudio::sox_effects::apply_effects_file( + path, effects, normalize, channels_first); } void save_audio_file( @@ -125,14 +84,11 @@ void save_audio_file( const c10::intrusive_ptr& signal, const double compression) { const auto tensor = signal->getTensor(); - const auto sample_rate = signal->getSampleRate(); - const auto channels_first = signal->getChannelsFirst(); validate_input_tensor(tensor); const auto filetype = get_filetype(file_name); - const auto signal_info = - get_signalinfo(tensor, sample_rate, channels_first, filetype); + const auto signal_info = get_signalinfo(signal.get(), filetype); const auto encoding_info = get_encodinginfo(filetype, tensor.dtype(), compression); @@ -148,22 +104,12 @@ void save_audio_file( throw std::runtime_error("Error saving audio file: failed to open file."); } - auto tensor_ = tensor; - if (channels_first) { - tensor_ = tensor_.t(); - } - - const int64_t frames_per_chunk = 65536; - for (int64_t i = 0; i < tensor_.size(0); i += frames_per_chunk) { - auto chunk = tensor_.index({Slice(i, i + frames_per_chunk), Slice()}); - chunk = unnormalize_wav(chunk).contiguous(); - - const size_t numel = chunk.numel(); - if (sox_write(sf, chunk.data_ptr(), numel) != numel) { - throw std::runtime_error( - "Error saving audio file: failed to write the entier buffer."); - } - } + torchaudio::sox_effects_chain::SoxEffectsChain chain( + /*input_encoding=*/get_encodinginfo("wav", tensor.dtype(), 0.), + /*output_encoding=*/sf->encoding); + chain.addInputTensor(signal.get()); + chain.addOutputFile(sf); + chain.run(); } } // namespace sox_io diff --git a/torchaudio/csrc/sox_utils.cpp b/torchaudio/csrc/sox_utils.cpp index c1fd8383a8..c6b73c5795 100644 --- a/torchaudio/csrc/sox_utils.cpp +++ b/torchaudio/csrc/sox_utils.cpp @@ -5,6 +5,62 @@ namespace torchaudio { namespace sox_utils { +void set_seed(const int64_t seed) { + sox_get_globals()->ranqd1 = static_cast(seed); +} + +void set_verbosity(const int64_t verbosity) { + sox_get_globals()->verbosity = static_cast(verbosity); +} + +void set_use_threads(const bool use_threads) { + sox_get_globals()->use_threads = static_cast(use_threads); +} + +void set_buffer_size(const int64_t buffer_size) { + sox_get_globals()->bufsiz = static_cast(buffer_size); +} + +std::vector> list_effects() { + std::vector> effects; + for (const sox_effect_fn_t* fns = sox_get_effect_fns(); *fns; ++fns) { + const sox_effect_handler_t* handler = (*fns)(); + if (handler && handler->name) { + if (UNSUPPORTED_EFFECTS.find(handler->name) == + UNSUPPORTED_EFFECTS.end()) { + effects.emplace_back(std::vector{ + handler->name, + handler->usage ? std::string(handler->usage) : std::string("")}); + } + } + } + return effects; +} + +std::vector list_write_formats() { + std::vector formats; + for (const sox_format_tab_t* fns = sox_get_format_fns(); fns->fn; ++fns) { + const sox_format_handler_t* handler = fns->fn(); + for (const char* const* names = handler->names; *names; ++names) { + if (!strchr(*names, '/') && handler->write) + formats.emplace_back(*names); + } + } + return formats; +} + +std::vector list_read_formats() { + std::vector formats; + for (const sox_format_tab_t* fns = sox_get_format_fns(); fns->fn; ++fns) { + const sox_format_handler_t* handler = fns->fn(); + for (const char* const* names = handler->names; *names; ++names) { + if (!strchr(*names, '/') && handler->read) + formats.emplace_back(*names); + } + } + return formats; +} + TensorSignal::TensorSignal( torch::Tensor tensor_, int64_t sample_rate_, @@ -205,13 +261,13 @@ unsigned get_precision( } sox_signalinfo_t get_signalinfo( - const torch::Tensor& tensor, - const int64_t sample_rate, - const bool channels_first, + const TensorSignal* signal, const std::string filetype) { + auto tensor = signal->getTensor(); return sox_signalinfo_t{ - /*rate=*/static_cast(sample_rate), - /*channels=*/static_cast(tensor.size(channels_first ? 0 : 1)), + /*rate=*/static_cast(signal->getSampleRate()), + /*channels=*/ + static_cast(tensor.size(signal->getChannelsFirst() ? 0 : 1)), /*precision=*/get_precision(filetype, tensor.dtype()), /*length=*/static_cast(tensor.numel())}; } diff --git a/torchaudio/csrc/sox_utils.h b/torchaudio/csrc/sox_utils.h index 665187c840..c9037e9e2f 100644 --- a/torchaudio/csrc/sox_utils.h +++ b/torchaudio/csrc/sox_utils.h @@ -7,6 +7,27 @@ namespace torchaudio { namespace sox_utils { +//////////////////////////////////////////////////////////////////////////////// +// APIs for Python interaction +//////////////////////////////////////////////////////////////////////////////// + +/// Set sox global options +void set_seed(const int64_t seed); + +void set_verbosity(const int64_t verbosity); + +void set_use_threads(const bool use_threads); + +void set_buffer_size(const int64_t buffer_size); + +std::vector> list_effects(); + +std::vector list_read_formats(); + +std::vector list_write_formats(); + +/// Class for exchanging signal infomation (tensor + meta data) between +/// C++ and Python for read/write operation. struct TensorSignal : torch::CustomClassHolder { torch::Tensor tensor; int64_t sample_rate; @@ -22,6 +43,13 @@ struct TensorSignal : torch::CustomClassHolder { bool getChannelsFirst() const; }; +//////////////////////////////////////////////////////////////////////////////// +// Utilities for sox_io / sox_effects implementations +//////////////////////////////////////////////////////////////////////////////// + +const std::unordered_set UNSUPPORTED_EFFECTS = + {"input", "output", "spectrogram", "noiseprof", "noisered", "splice"}; + /// helper class to automatically close sox_format_t* struct SoxFormat { explicit SoxFormat(sox_format_t* fd) noexcept; @@ -84,9 +112,7 @@ const std::string get_filetype(const std::string path); /// Get sox_signalinfo_t for passing a torch::Tensor object. sox_signalinfo_t get_signalinfo( - const torch::Tensor& tensor, - const int64_t sample_rate, - const bool channels_first, + const TensorSignal* signal, const std::string filetype); /// Get sox_encofinginfo_t for saving audoi file diff --git a/torchaudio/datasets/__init__.py b/torchaudio/datasets/__init__.py index 942f2c83a1..187142db46 100644 --- a/torchaudio/datasets/__init__.py +++ b/torchaudio/datasets/__init__.py @@ -7,6 +7,7 @@ from .yesno import YESNO from .ljspeech import LJSPEECH from .cmuarctic import CMUARCTIC +from .libritts import LIBRITTS __all__ = ( "COMMONVOICE", @@ -17,6 +18,7 @@ "LJSPEECH", "GTZAN", "CMUARCTIC", + "LIBRITTS" "diskcache_iterator", "bg_iterator", ) diff --git a/torchaudio/datasets/gtzan.py b/torchaudio/datasets/gtzan.py index 13d7e8b68b..e031801bc1 100644 --- a/torchaudio/datasets/gtzan.py +++ b/torchaudio/datasets/gtzan.py @@ -8,7 +8,6 @@ from torchaudio.datasets.utils import ( download_url, extract_archive, - walk_files, ) # The following lists prefixed with `filtered_` provide a filtered split @@ -22,6 +21,19 @@ # Those are used when GTZAN is initialised with the `filtered` keyword. # The split was taken from (github) jordipons/sklearn-audio-transfer-learning. +gtzan_genres = [ + "blues", + "classical", + "country", + "disco", + "hiphop", + "jazz", + "metal", + "pop", + "reggae", + "rock", +] + filtered_test = [ "blues.00012", "blues.00013", @@ -964,7 +976,9 @@ URL = "http://opihi.cs.uvic.ca/sound/genres.tar.gz" FOLDER_IN_ARCHIVE = "genres" -_CHECKSUMS = {"http://opihi.cs.uvic.ca/sound/genres.tar.gz": "5b3d6dddb579ab49814ab86dba69e7c7"} +_CHECKSUMS = { + "http://opihi.cs.uvic.ca/sound/genres.tar.gz": "5b3d6dddb579ab49814ab86dba69e7c7" +} def load_gtzan_item(fileid: str, path: str, ext_audio: str) -> Tuple[Tensor, str]: @@ -1032,10 +1046,33 @@ def __init__( ) if self.subset is None: - walker = walk_files( - self._path, suffix=self._ext_audio, prefix=False, remove_suffix=True - ) - self._walker = list(walker) + # Check every subdirectory under dataset root + # which has the same name as the genres in + # GTZAN (e.g. `root_dir'/blues/, `root_dir'/rock, etc.) + # This lets users remove or move around song files, + # useful when e.g. they want to use only some of the files + # in a genre or want to label other files with a different + # genre. + self._walker = [] + + root = os.path.expanduser(self._path) + + for directory in gtzan_genres: + fulldir = os.path.join(root, directory) + + if not os.path.exists(fulldir): + continue + + songs_in_genre = os.listdir(fulldir) + songs_in_genre.sort() + for fname in songs_in_genre: + name, ext = os.path.splitext(fname) + if ext.lower() == ".wav" and "." in name: + # Check whether the file is of the form + # `gtzan_genre`.`5 digit number`.wav + genre, num = name.split(".") + if genre in gtzan_genres and len(num) == 5 and num.isdigit(): + self._walker.append(name) else: if self.subset == "training": self._walker = filtered_train diff --git a/torchaudio/datasets/libritts.py b/torchaudio/datasets/libritts.py new file mode 100644 index 0000000000..a37d5528fb --- /dev/null +++ b/torchaudio/datasets/libritts.py @@ -0,0 +1,131 @@ +import os +from typing import Tuple + +import torchaudio +from torch import Tensor +from torch.utils.data import Dataset +from torchaudio.datasets.utils import ( + download_url, + extract_archive, + walk_files, +) + +URL = "train-clean-100" +FOLDER_IN_ARCHIVE = "LibriTTS" +_CHECKSUMS = { + "http://www.openslr.org/60/dev-clean.tar.gz": "0c3076c1e5245bb3f0af7d82087ee207", + "http://www.openslr.org/60/dev-other.tar.gz": "815555d8d75995782ac3ccd7f047213d", + "http://www.openslr.org/60/test-clean.tar.gz": "7bed3bdb047c4c197f1ad3bc412db59f", + "http://www.openslr.org/60/test-other.tar.gz": "ae3258249472a13b5abef2a816f733e4", + "http://www.openslr.org/60/train-clean-100.tar.gz": "4a8c202b78fe1bc0c47916a98f3a2ea8", + "http://www.openslr.org/60/train-clean-360.tar.gz": "a84ef10ddade5fd25df69596a2767b2d", + "http://www.openslr.org/60/train-other-500.tar.gz": "7b181dd5ace343a5f38427999684aa6f", +} + + +def load_libritts_item( + fileid: str, + path: str, + ext_audio: str, + ext_original_txt: str, + ext_normalized_txt: str, +) -> Tuple[Tensor, int, str, str, int, int, str]: + speaker_id, chapter_id, segment_id, utterance_id = fileid.split("_") + utterance_id = fileid + + normalized_text = utterance_id + ext_normalized_txt + normalized_text = os.path.join(path, speaker_id, chapter_id, normalized_text) + + original_text = utterance_id + ext_original_txt + original_text = os.path.join(path, speaker_id, chapter_id, original_text) + + file_audio = utterance_id + ext_audio + file_audio = os.path.join(path, speaker_id, chapter_id, file_audio) + + # Load audio + waveform, sample_rate = torchaudio.load(file_audio) + + # Load original text + with open(original_text) as ft: + original_text = ft.readline() + + # Load normalized text + with open(normalized_text, "r") as ft: + normalized_text = ft.readline() + + return ( + waveform, + sample_rate, + original_text, + normalized_text, + int(speaker_id), + int(chapter_id), + utterance_id, + ) + + +class LIBRITTS(Dataset): + """ + Create a Dataset for LibriTTS. Each item is a tuple of the form: + waveform, sample_rate, original_text, normalized_text, speaker_id, chapter_id, utterance_id + """ + + _ext_original_txt = ".original.txt" + _ext_normalized_txt = ".normalized.txt" + _ext_audio = ".wav" + + def __init__( + self, + root: str, + url: str = URL, + folder_in_archive: str = FOLDER_IN_ARCHIVE, + download: bool = False, + ) -> None: + + if url in [ + "dev-clean", + "dev-other", + "test-clean", + "test-other", + "train-clean-100", + "train-clean-360", + "train-other-500", + ]: + + ext_archive = ".tar.gz" + base_url = "http://www.openslr.org/resources/60/" + + url = os.path.join(base_url, url + ext_archive) + + basename = os.path.basename(url) + archive = os.path.join(root, basename) + + basename = basename.split(".")[0] + folder_in_archive = os.path.join(folder_in_archive, basename) + + self._path = os.path.join(root, folder_in_archive) + + if download: + if not os.path.isdir(self._path): + if not os.path.isfile(archive): + checksum = _CHECKSUMS.get(url, None) + download_url(url, root, hash_value=checksum) + extract_archive(archive) + + walker = walk_files( + self._path, suffix=self._ext_audio, prefix=False, remove_suffix=True + ) + self._walker = list(walker) + + def __getitem__(self, n: int) -> Tuple[Tensor, int, str, str, int, int, str]: + fileid = self._walker[n] + return load_libritts_item( + fileid, + self._path, + self._ext_audio, + self._ext_original_txt, + self._ext_normalized_txt, + ) + + def __len__(self) -> int: + return len(self._walker) diff --git a/torchaudio/datasets/utils.py b/torchaudio/datasets/utils.py index 2840b43471..d00ffc143b 100644 --- a/torchaudio/datasets/utils.py +++ b/torchaudio/datasets/utils.py @@ -264,7 +264,13 @@ def walk_files(root: str, root = os.path.expanduser(root) - for dirpath, _, files in os.walk(root): + for dirpath, dirs, files in os.walk(root): + dirs.sort() + # `dirs` is the list used in os.walk function and by sorting it in-place here, we change the + # behavior of os.walk to traverse sub directory alphabetically + # see also + # https://stackoverflow.com/questions/6670029/can-i-force-python3s-os-walk-to-visit-directories-in-alphabetical-order-how#comment71993866_6670926 + files.sort() for f in files: if f.endswith(suffix): diff --git a/torchaudio/functional.py b/torchaudio/functional.py index 78c8c594c9..713544cc74 100644 --- a/torchaudio/functional.py +++ b/torchaudio/functional.py @@ -1646,11 +1646,11 @@ def compute_deltas( r"""Compute delta coefficients of a tensor, usually a spectrogram: .. math:: - d_t = \frac{\sum_{n=1}^{\text{N}} n (c_{t+n} - c_{t-n})}{2 \sum_{n=1}^{\text{N} n^2} + d_t = \frac{\sum_{n=1}^{\text{N}} n (c_{t+n} - c_{t-n})}{2 \sum_{n=1}^{\text{N}} n^2} where :math:`d_t` is the deltas at time :math:`t`, :math:`c_t` is the spectrogram coeffcients at time :math:`t`, - :math:`N` is (`win_length`-1)//2. + :math:`N` is ``(win_length-1)//2``. Args: specgram (Tensor): Tensor of audio of dimension (..., freq, time) diff --git a/torchaudio/models/_wavernn.py b/torchaudio/models/_wavernn.py index cd2e89a10c..37a06edfc3 100644 --- a/torchaudio/models/_wavernn.py +++ b/torchaudio/models/_wavernn.py @@ -1,4 +1,4 @@ -from typing import List +from typing import List, Tuple import torch from torch import Tensor @@ -13,7 +13,7 @@ class _ResBlock(nn.Module): The paper link is https://arxiv.org/pdf/1512.03385.pdf. Args: - n_freq: the number of bins in a spectrogram (default=128) + n_freq: the number of bins in a spectrogram. (Default: ``128``) Examples >>> resblock = _ResBlock() @@ -48,11 +48,11 @@ class _MelResNet(nn.Module): r"""MelResNet layer uses a stack of ResBlocks on spectrogram. Args: - n_res_block: the number of ResBlock in stack (default=10) - n_freq: the number of bins in a spectrogram (default=128) - n_hidden: the number of hidden dimensions (default=128) - n_output: the number of output dimensions (default=128) - kernel_size: the number of kernel size in the first Conv1d layer (default=5) + n_res_block: the number of ResBlock in stack. (Default: ``10``) + n_freq: the number of bins in a spectrogram. (Default: ``128``) + n_hidden: the number of hidden dimensions of resblock. (Default: ``128``) + n_output: the number of output dimensions of melresnet. (Default: ``128``) + kernel_size: the number of kernel size in the first Conv1d layer. (Default: ``5``) Examples >>> melresnet = _MelResNet() @@ -129,12 +129,12 @@ class _UpsampleNetwork(nn.Module): r"""Upscale the dimensions of a spectrogram. Args: - upsample_scales: the list of upsample scales - n_res_block: the number of ResBlock in stack (default=10) - n_freq: the number of bins in a spectrogram (default=128) - n_hidden: the number of hidden dimensions (default=128) - n_output: the number of output dimensions (default=128) - kernel_size: the number of kernel size in the first Conv1d layer (default=5) + upsample_scales: the list of upsample scales. + n_res_block: the number of ResBlock in stack. (Default: ``10``) + n_freq: the number of bins in a spectrogram. (Default: ``128``) + n_hidden: the number of hidden dimensions of resblock. (Default: ``128``) + n_output: the number of output dimensions of melresnet. (Default: ``128``) + kernel_size: the number of kernel size in the first Conv1d layer. (Default: ``5``) Examples >>> upsamplenetwork = _UpsampleNetwork(upsample_scales=[4, 4, 16]) @@ -172,7 +172,7 @@ def __init__(self, up_layers.append(conv) self.upsample_layers = nn.Sequential(*up_layers) - def forward(self, specgram: Tensor) -> Tensor: + def forward(self, specgram: Tensor) -> Tuple[Tensor, Tensor]: r"""Pass the input through the _UpsampleNetwork layer. Args: @@ -204,32 +204,29 @@ class _WaveRNN(nn.Module): `upsample_scales` must equal `hop_length`. Args: - upsample_scales: the list of upsample scales - n_bits: the bits of output waveform - sample_rate: the rate of audio dimensions (samples per second) - hop_length: the number of samples between the starts of consecutive frames - n_res_block: the number of ResBlock in stack (default=10) - n_rnn: the dimension of RNN layer (default=512) - n_fc: the dimension of fully connected layer (default=512) - kernel_size: the number of kernel size in the first Conv1d layer (default=5) - n_freq: the number of bins in a spectrogram (default=128) - n_hidden: the number of hidden dimensions (default=128) - n_output: the number of output dimensions (default=128) - mode: the mode of waveform in ['waveform', 'mol'] (default='waveform') + upsample_scales: the list of upsample scales. + n_classes: the number of output classes. + hop_length: the number of samples between the starts of consecutive frames. + n_res_block: the number of ResBlock in stack. (Default: ``10``) + n_rnn: the dimension of RNN layer. (Default: ``512``) + n_fc: the dimension of fully connected layer. (Default: ``512``) + kernel_size: the number of kernel size in the first Conv1d layer. (Default: ``5``) + n_freq: the number of bins in a spectrogram. (Default: ``128``) + n_hidden: the number of hidden dimensions of resblock. (Default: ``128``) + n_output: the number of output dimensions of melresnet. (Default: ``128``) Example - >>> wavernn = _waveRNN(upsample_scales=[5,5,8], n_bits=9, sample_rate=24000, hop_length=200) + >>> wavernn = _waveRNN(upsample_scales=[5,5,8], n_classes=512, hop_length=200) >>> waveform, sample_rate = torchaudio.load(file) >>> # waveform shape: (n_batch, n_channel, (n_time - kernel_size + 1) * hop_length) >>> specgram = MelSpectrogram(sample_rate)(waveform) # shape: (n_batch, n_channel, n_freq, n_time) >>> output = wavernn(waveform, specgram) - >>> # output shape: (n_batch, n_channel, (n_time - kernel_size + 1) * hop_length, 2 ** n_bits) + >>> # output shape: (n_batch, n_channel, (n_time - kernel_size + 1) * hop_length, n_classes) """ def __init__(self, upsample_scales: List[int], - n_bits: int, - sample_rate: int, + n_classes: int, hop_length: int, n_res_block: int = 10, n_rnn: int = 512, @@ -237,24 +234,14 @@ def __init__(self, kernel_size: int = 5, n_freq: int = 128, n_hidden: int = 128, - n_output: int = 128, - mode: str = 'waveform') -> None: + n_output: int = 128) -> None: super().__init__() - self.mode = mode self.kernel_size = kernel_size - - if self.mode == 'waveform': - self.n_classes = 2 ** n_bits - elif self.mode == 'mol': - self.n_classes = 30 - else: - raise ValueError(f"Expected mode: `waveform` or `mol`, but found {self.mode}") - self.n_rnn = n_rnn self.n_aux = n_output // 4 self.hop_length = hop_length - self.sample_rate = sample_rate + self.n_classes = n_classes total_scale = 1 for upsample_scale in upsample_scales: @@ -262,7 +249,12 @@ def __init__(self, if total_scale != self.hop_length: raise ValueError(f"Expected: total_scale == hop_length, but found {total_scale} != {hop_length}") - self.upsample = _UpsampleNetwork(upsample_scales, n_res_block, n_freq, n_hidden, n_output, kernel_size) + self.upsample = _UpsampleNetwork(upsample_scales, + n_res_block, + n_freq, + n_hidden, + n_output, + kernel_size) self.fc = nn.Linear(n_freq + self.n_aux + 1, n_rnn) self.rnn1 = nn.GRU(n_rnn, n_rnn, batch_first=True) @@ -283,7 +275,7 @@ def forward(self, waveform: Tensor, specgram: Tensor) -> Tensor: specgram: the input spectrogram to the _WaveRNN layer (n_batch, 1, n_freq, n_time) Return: - Tensor shape: (n_batch, 1, (n_time - kernel_size + 1) * hop_length, 2 ** n_bits) + Tensor shape: (n_batch, 1, (n_time - kernel_size + 1) * hop_length, n_classes) """ assert waveform.size(1) == 1, 'Require the input channel of waveform is 1' diff --git a/torchaudio/models/wav2letter.py b/torchaudio/models/wav2letter.py index 3466e42dd2..acd4dfe4b3 100644 --- a/torchaudio/models/wav2letter.py +++ b/torchaudio/models/wav2letter.py @@ -1,5 +1,3 @@ -from typing import Optional - from torch import Tensor from torch import nn @@ -7,8 +5,9 @@ class Wav2Letter(nn.Module): - r"""Wav2Letter model architecture from the `"Wav2Letter: an End-to-End ConvNet-based Speech Recognition System" - `_ paper. + r"""Wav2Letter model architecture from the `Wav2Letter an End-to-End ConvNet-based Speech Recognition System`_. + + .. _Wav2Letter an End-to-End ConvNet-based Speech Recognition System: https://arxiv.org/abs/1609.03193 :math:`\text{padding} = \frac{\text{ceil}(\text{kernel} - \text{stride})}{2}` @@ -63,7 +62,7 @@ def __init__(self, num_classes: int = 40, def forward(self, x: Tensor) -> Tensor: r""" Args: - x (Tensor): Tensor of dimension (batch_size, num_features, input_length). + x (torch.Tensor): Tensor of dimension (batch_size, num_features, input_length). Returns: Tensor: Predictor tensor of dimension (batch_size, number_of_classes, input_length). diff --git a/torchaudio/sox_effects/__init__.py b/torchaudio/sox_effects/__init__.py index 507dc5c3af..d9650173c5 100644 --- a/torchaudio/sox_effects/__init__.py +++ b/torchaudio/sox_effects/__init__.py @@ -3,6 +3,8 @@ init_sox_effects, shutdown_sox_effects, effect_names, + apply_effects_tensor, + apply_effects_file, SoxEffect, SoxEffectsChain, ) diff --git a/torchaudio/sox_effects/sox_effects.py b/torchaudio/sox_effects/sox_effects.py index 0aee312126..9262786541 100644 --- a/torchaudio/sox_effects/sox_effects.py +++ b/torchaudio/sox_effects/sox_effects.py @@ -7,37 +7,37 @@ module_utils as _mod_utils, misc_ops as _misc_ops, ) +from torchaudio.utils.sox_utils import list_effects + if _mod_utils.is_module_available('torchaudio._torchaudio'): from torchaudio import _torchaudio @_mod_utils.requires_module('torchaudio._torchaudio') -def init_sox_effects() -> None: - """Initialize resources required to use ``SoxEffectsChain`` - - You do not need to call this function manually. It is called automatically. - - Once initialized, you do not need to call this function again across the multiple call of - ``SoxEffectsChain.sox_build_flow_effects``, though it is safe to do so as long as - ``shutdown_sox_effects`` is not called yet. - Once ``shutdown_sox_effects`` is called, you can no longer use SoX effects and - initializing again will result in error. +def init_sox_effects(): + """Initialize resources required to use sox effects. Note: - This function is not required for simple loading. + You do not need to call this function manually. It is called automatically. + + Once initialized, you do not need to call this function again across the multiple uses of + sox effects though it is safe to do so as long as :func:`shutdown_sox_effects` is not called yet. + Once :func:`shutdown_sox_effects` is called, you can no longer use SoX effects and initializing + again will result in error. """ torch.ops.torchaudio.sox_effects_initialize_sox_effects() @_mod_utils.requires_module("torchaudio._torchaudio") -def shutdown_sox_effects() -> None: - """Clean up resources required to use ``SoxEffectsChain`` +def shutdown_sox_effects(): + """Clean up resources required to use sox effects. - You do not need to call this function manually. It is called automatically. + Note: + You do not need to call this function manually. It is called automatically. It is safe to call this function multiple times. - Once ``shutdown_sox_effects`` is called, you can no longer use SoX effects and + Once :py:func:`shutdown_sox_effects` is called, you can no longer use SoX effects and initializing again will result in error. """ torch.ops.torchaudio.sox_effects_shutdown_sox_effects() @@ -47,18 +47,227 @@ def shutdown_sox_effects() -> None: def effect_names() -> List[str]: """Gets list of valid sox effect names - Returns: list[str] + Returns: + List[str]: list of available effect names. Example - >>> EFFECT_NAMES = torchaudio.sox_effects.effect_names() + >>> torchaudio.sox_effects.effect_names() + ['allpass', 'band', 'bandpass', ... ] """ - return torch.ops.torchaudio.sox_effects_list_effects() + return list(list_effects().keys()) @_mod_utils.requires_module('torchaudio._torchaudio') +def apply_effects_tensor( + tensor: torch.Tensor, + sample_rate: int, + effects: List[List[str]], + channels_first: bool = True, +) -> Tuple[torch.Tensor, int]: + """Apply sox effects to given Tensor + + Note: + This function works in the way very similar to ``sox`` command, however there are slight + differences. For example, ``sox`` commnad adds certain effects automatically (such as + ``rate`` effect after ``speed`` and ``pitch`` and other effects), but this function does + only applies the given effects. (Therefore, to actually apply ``speed`` effect, you also + need to give ``rate`` effect with desired sampling rate.) + + Args: + tensor (torch.Tensor): Input 2D Tensor. + sample_rate (int): Sample rate + effects (List[List[str]]): List of effects. + channels_first (bool): Indicates if the input Tensor's dimension is + ``[channels, time]`` or ``[time, channels]`` + + Returns: + Tuple[torch.Tensor, int]: Resulting Tensor and sample rate. + The resulting Tensor has the same ``dtype`` as the input Tensor, and + the same channels order. The shape of the Tensor can be different based on the + effects applied. Sample rate can also be different based on the effects applied. + + Example - Basic usage + >>> + >>> # Defines the effects to apply + >>> effects = [ + ... ['gain', '-n'], # normalises to 0dB + ... ['pitch', '5'], # 5 cent pitch shift + ... ['rate', '8000'], # resample to 8000 Hz + ... ] + >>> + >>> # Generate pseudo wave: + >>> # normalized, channels first, 2ch, sampling rate 16000, 1 second + >>> sample_rate = 16000 + >>> waveform = 2 * torch.rand([2, sample_rate * 1]) - 1 + >>> waveform.shape + torch.Size([2, 16000]) + >>> waveform + tensor([[ 0.3138, 0.7620, -0.9019, ..., -0.7495, -0.4935, 0.5442], + [-0.0832, 0.0061, 0.8233, ..., -0.5176, -0.9140, -0.2434]]) + >>> + >>> # Apply effects + >>> waveform, sample_rate = apply_effects_tensor( + ... wave_form, sample_rate, effects, channels_first=True) + >>> + >>> # Check the result + >>> # The new waveform is sampling rate 8000, 1 second. + >>> # normalization and channel order are preserved + >>> waveform.shape + torch.Size([2, 8000]) + >>> waveform + tensor([[ 0.5054, -0.5518, -0.4800, ..., -0.0076, 0.0096, -0.0110], + [ 0.1331, 0.0436, -0.3783, ..., -0.0035, 0.0012, 0.0008]]) + >>> sample_rate + 8000 + + Example - Torchscript-able transform + >>> + >>> # Use `apply_effects_tensor` in `torch.nn.Module` and dump it to file, + >>> # then run sox effect via Torchscript runtime. + >>> + >>> class SoxEffectTransform(torch.nn.Module): + ... effects: List[List[str]] + ... + ... def __init__(self, effects: List[List[str]]): + ... super().__init__() + ... self.effects = effects + ... + ... def forward(self, tensor: torch.Tensor, sample_rate: int): + ... return sox_effects.apply_effects_tensor( + ... tensor, sample_rate, self.effects) + ... + ... + >>> # Create transform object + >>> effects = [ + ... ["lowpass", "-1", "300"], # apply single-pole lowpass filter + ... ["rate", "8000"], # change sample rate to 8000 + ... ] + >>> transform = SoxEffectTensorTransform(effects, input_sample_rate) + >>> + >>> # Dump it to file and load + >>> path = 'sox_effect.zip' + >>> torch.jit.script(trans).save(path) + >>> transform = torch.jit.load(path) + >>> + >>>> # Run transform + >>> waveform, input_sample_rate = torchaudio.load("input.wav") + >>> waveform, sample_rate = transform(waveform, input_sample_rate) + >>> assert sample_rate == 8000 + """ + in_signal = torch.classes.torchaudio.TensorSignal(tensor, sample_rate, channels_first) + out_signal = torch.ops.torchaudio.sox_effects_apply_effects_tensor(in_signal, effects) + return out_signal.get_tensor(), out_signal.get_sample_rate() + + +@_mod_utils.requires_module('torchaudio._torchaudio') +def apply_effects_file( + path: str, + effects: List[List[str]], + normalize: bool = True, + channels_first: bool = True, +) -> Tuple[torch.Tensor, int]: + """Apply sox effects to the audio file and load the resulting data as Tensor + + Note: + This function works in the way very similar to ``sox`` command, however there are slight + differences. For example, ``sox`` commnad adds certain effects automatically (such as + ``rate`` effect after ``speed``, ``pitch`` etc), but this function only applies the given + effects. Therefore, to actually apply ``speed`` effect, you also need to give ``rate`` + effect with desired sampling rate, because internally, ``speed`` effects only alter sampling + rate and leave samples untouched. + + Args: + path (str): Path to the audio file. + effects (List[List[str]]): List of effects. + normalize (bool): + When ``True``, this function always return ``float32``, and sample values are + normalized to ``[-1.0, 1.0]``. + If input file is integer WAV, giving ``False`` will change the resulting Tensor type to + integer type. This argument has no effect for formats other + than integer WAV type. + channels_first (bool): When True, the returned Tensor has dimension ``[channel, time]``. + Otherwise, the returned Tensor's dimension is ``[time, channel]``. + + Returns: + Tuple[torch.Tensor, int]: Resulting Tensor and sample rate. + If ``normalize=True``, the resulting Tensor is always ``float32`` type. + If ``normalize=False`` and the input audio file is of integer WAV file, then the + resulting Tensor has corresponding integer type. (Note 24 bit integer type is not supported) + If ``channels_first=True``, the resulting Tensor has dimension ``[channel, time]``, + otherwise ``[time, channel]``. + + Example - Basic usage + >>> + >>> # Defines the effects to apply + >>> effects = [ + ... ['gain', '-n'], # normalises to 0dB + ... ['pitch', '5'], # 5 cent pitch shift + ... ['rate', '8000'], # resample to 8000 Hz + ... ] + >>> + >>> # Apply effects and load data with channels_first=True + >>> waveform, sample_rate = apply_effects_file("data.wav", effects, channels_first=True) + >>> + >>> # Check the result + >>> waveform.shape + torch.Size([2, 8000]) + >>> waveform + tensor([[ 5.1151e-03, 1.8073e-02, 2.2188e-02, ..., 1.0431e-07, + -1.4761e-07, 1.8114e-07], + [-2.6924e-03, 2.1860e-03, 1.0650e-02, ..., 6.4122e-07, + -5.6159e-07, 4.8103e-07]]) + >>> sample_rate + 8000 + + Example - Apply random speed perturbation to dataset + >>> + >>> # Load data from file, apply random speed perturbation + >>> class RandomPerturbationFile(torch.utils.data.Dataset): + ... \"\"\"Given flist, apply random speed perturbation + ... + ... Suppose all the input files are at least one second long. + ... \"\"\" + ... def __init__(self, flist: List[str], sample_rate: int): + ... super().__init__() + ... self.flist = flist + ... self.sample_rate = sample_rate + ... self.rng = None + ... + ... def __getitem__(self, index): + ... speed = self.rng.uniform(0.5, 2.0) + ... effects = [ + ... ['gain', '-n', '-10'], # apply 10 db attenuation + ... ['remix', '-'], # merge all the channels + ... ['speed', f'{speed:.5f}'], # duration is now 0.5 ~ 2.0 seconds. + ... ['rate', f'{self.sample_rate}'], + ... ['pad', '0', '1.5'], # add 1.5 seconds silence at the end + ... ['trim', '0', '2'], # get the first 2 seconds + ... ] + ... waveform, _ = torchaudio.sox_effects.apply_effects_file( + ... self.flist[index], effects) + ... return waveform + ... + ... def __len__(self): + ... return len(self.flist) + ... + >>> dataset = RandomPerturbationFile(file_list, sample_rate=8000) + >>> loader = torch.utils.data.DataLoader(dataset, batch_size=32) + >>> for batch in loader: + >>> pass + """ + signal = torch.ops.torchaudio.sox_effects_apply_effects_file(path, effects, normalize, channels_first) + return signal.get_tensor(), signal.get_sample_rate() + + +@_mod_utils.requires_module('torchaudio._torchaudio') +@_mod_utils.deprecated('Please migrate to `apply_effects_file` or `apply_effects_tensor`.') def SoxEffect(): r"""Create an object for passing sox effect information between python and c++ + Warning: + This function is deprecated. + Please migrate to :func:`apply_effects_file` or :func:`apply_effects_tensor`. + Returns: SoxEffect: An object with the following attributes: ename (str) which is the name of effect, and eopts (List[str]) which is a list of effect options. @@ -66,49 +275,60 @@ def SoxEffect(): return _torchaudio.SoxEffect() +@_mod_utils.deprecated('Please migrate to `apply_effects_file` or `apply_effects_tensor`.') class SoxEffectsChain(object): r"""SoX effects chain class. + Warning: + This class is deprecated. + Please migrate to :func:`apply_effects_file` or :func:`apply_effects_tensor`. + Args: - normalization (bool, number, or callable, optional): If boolean `True`, then output is divided by `1 << 31` - (assumes signed 32-bit audio), and normalizes to `[-1, 1]`. If `number`, then output is divided by that - number. If `callable`, then the output is passed as a parameter to the given function, then the - output is divided by the result. (Default: ``True``) - channels_first (bool, optional): Set channels first or length first in result. (Default: ``True``) - out_siginfo (sox_signalinfo_t, optional): a sox_signalinfo_t type, which could be helpful if the - audio type cannot be automatically determined. (Default: ``None``) - out_encinfo (sox_encodinginfo_t, optional): a sox_encodinginfo_t type, which could be set if the - audio type cannot be automatically determined. (Default: ``None``) - filetype (str, optional): a filetype or extension to be set if sox cannot determine it - automatically. . (Default: ``'raw'``) + normalization (bool, number, or callable, optional): + If boolean ``True``, then output is divided by ``1 << 31`` + (assumes signed 32-bit audio), and normalizes to ``[-1, 1]``. + If ``number``, then output is divided by that number. + If ``callable``, then the output is passed as a parameter to the given function, then + the output is divided by the result. (Default: ``True``) + channels_first (bool, optional): + Set channels first or length first in result. (Default: ``True``) + out_siginfo (sox_signalinfo_t, optional): + a sox_signalinfo_t type, which could be helpful if the audio type cannot be + automatically determined. (Default: ``None``) + out_encinfo (sox_encodinginfo_t, optional): + a sox_encodinginfo_t type, which could be set if the audio type cannot be + automatically determined. (Default: ``None``) + filetype (str, optional): + a filetype or extension to be set if sox cannot determine it automatically. + (Default: ``'raw'``) Returns: - Tuple[Tensor, int]: An output Tensor of size `[C x L]` or `[L x C]` where L is the number + Tuple[Tensor, int]: + An output Tensor of size ``[C x L]`` or ``[L x C]`` where L is the number of audio frames and C is the number of channels. An integer which is the sample rate of the audio (as listed in the metadata of the file) Example >>> class MyDataset(Dataset): - >>> def __init__(self, audiodir_path): - >>> self.data = [os.path.join(audiodir_path, fn) for fn in os.listdir(audiodir_path)] - >>> self.E = torchaudio.sox_effects.SoxEffectsChain() - >>> self.E.append_effect_to_chain("rate", [16000]) # resample to 16000hz - >>> self.E.append_effect_to_chain("channels", ["1"]) # mono signal - >>> def __getitem__(self, index): - >>> fn = self.data[index] - >>> self.E.set_input_file(fn) - >>> x, sr = self.E.sox_build_flow_effects() - >>> return x, sr - >>> - >>> def __len__(self): - >>> return len(self.data) - >>> - >>> torchaudio.initialize_sox() + ... def __init__(self, audiodir_path): + ... self.data = [ + ... os.path.join(audiodir_path, fn) + ... for fn in os.listdir(audiodir_path)] + ... self.E = torchaudio.sox_effects.SoxEffectsChain() + ... self.E.append_effect_to_chain("rate", [16000]) # resample to 16000hz + ... self.E.append_effect_to_chain("channels", ["1"]) # mono signal + ... def __getitem__(self, index): + ... fn = self.data[index] + ... self.E.set_input_file(fn) + ... x, sr = self.E.sox_build_flow_effects() + ... return x, sr + ... + ... def __len__(self): + ... return len(self.data) + ... >>> ds = MyDataset(path_to_audio_files) >>> for sig, sr in ds: - >>> [do something here] - >>> torchaudio.shutdown_sox() - + ... pass """ EFFECTS_UNIMPLEMENTED = {"spectrogram", "splice", "noiseprof", "fir"} @@ -165,9 +385,9 @@ def sox_build_flow_effects(self, out (Tensor, optional): Where the output will be written to. (Default: ``None``) Returns: - Tuple[Tensor, int]: An output Tensor of size `[C x L]` or `[L x C]` where L is the number - of audio frames and C is the number of channels. An integer which is the sample rate of the - audio (as listed in the metadata of the file) + Tuple[Tensor, int]: An output Tensor of size `[C x L]` or `[L x C]` where + L is the number of audio frames and C is the number of channels. + An integer which is the sample rate of the audio (as listed in the metadata of the file) """ # initialize output tensor if out is not None: diff --git a/torchaudio/transforms.py b/torchaudio/transforms.py index f34f5c468e..72e55c94c8 100644 --- a/torchaudio/transforms.py +++ b/torchaudio/transforms.py @@ -86,20 +86,8 @@ def forward(self, waveform: Tensor) -> Tensor: class GriffinLim(torch.nn.Module): r"""Compute waveform from a linear scale magnitude spectrogram using the Griffin-Lim transformation. - Implementation ported from `librosa`. - .. [1] McFee, Brian, Colin Raffel, Dawen Liang, Daniel PW Ellis, Matt McVicar, Eric Battenberg, and Oriol Nieto. - "librosa: Audio and music signal analysis in python." - In Proceedings of the 14th python in science conference, pp. 18-25. 2015. - - .. [2] Perraudin, N., Balazs, P., & Sรธndergaard, P. L. - "A fast Griffin-Lim algorithm," - IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (pp. 1-4), - Oct. 2013. - - .. [3] D. W. Griffin and J. S. Lim, - "Signal estimation from modified short-time Fourier transform," - IEEE Trans. ASSP, vol.32, no.2, pp.236โ€“243, Apr. 1984. + Implementation ported from ``librosa`` [1]_, [2]_, [3]_. Args: n_fft (int, optional): Size of FFT, creates ``n_fft // 2 + 1`` bins. (Default: ``400``) @@ -117,6 +105,24 @@ class GriffinLim(torch.nn.Module): Values near 1 can lead to faster convergence, but above 1 may not converge. (Default: ``0.99``) length (int, optional): Array length of the expected output. (Default: ``None``) rand_init (bool, optional): Initializes phase randomly if True and to zero otherwise. (Default: ``True``) + + References: + .. [1] + | McFee, Brian, Colin Raffel, Dawen Liang, Daniel PW Ellis, Matt McVicar, Eric Battenberg, + and Oriol Nieto. + | "librosa: Audio and music signal analysis in python." + | In Proceedings of the 14th python in science conference, pp. 18-25. 2015. + + .. [2] + | Perraudin, N., Balazs, P., & Sรธndergaard, P. L. + | "A fast Griffin-Lim algorithm," + | IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (pp. 1-4), + | Oct. 2013. + + .. [3] + | D. W. Griffin and J. S. Lim, + | "Signal estimation from modified short-time Fourier transform," + | IEEE Trans. ASSP, vol.32, no.2, pp.236โ€“243, Apr. 1984. """ __constants__ = ['n_fft', 'n_iter', 'win_length', 'hop_length', 'power', 'normalized', 'length', 'momentum', 'rand_init'] @@ -153,8 +159,9 @@ def __init__(self, def forward(self, specgram: Tensor) -> Tensor: r""" Args: - specgram (Tensor): A magnitude-only STFT spectrogram of dimension (..., freq, frames) - where freq is ``n_fft // 2 + 1``. + specgram (Tensor): + A magnitude-only STFT spectrogram of dimension (..., freq, frames) + where freq is ``n_fft // 2 + 1``. Returns: Tensor: waveform of (..., time), where time equals the ``length`` parameter if given. diff --git a/torchaudio/utils/__init__.py b/torchaudio/utils/__init__.py new file mode 100644 index 0000000000..bc11f86893 --- /dev/null +++ b/torchaudio/utils/__init__.py @@ -0,0 +1,9 @@ +from . import ( + sox_utils, +) + +from torchaudio._internal import module_utils as _mod_utils + + +if _mod_utils.is_module_available('torchaudio._torchaudio'): + sox_utils.set_verbosity(1) diff --git a/torchaudio/utils/sox_utils.py b/torchaudio/utils/sox_utils.py new file mode 100644 index 0000000000..59090e4b88 --- /dev/null +++ b/torchaudio/utils/sox_utils.py @@ -0,0 +1,94 @@ +from typing import List, Dict + +import torch + +from torchaudio._internal import ( + module_utils as _mod_utils, +) + + +@_mod_utils.requires_module('torchaudio._torchaudio') +def set_seed(seed: int): + """Set libsox's PRNG + + Args: + seed (int): seed value. valid range is int32. + + See Also: + http://sox.sourceforge.net/sox.html + """ + torch.ops.torchaudio.sox_utils_set_seed(seed) + + +@_mod_utils.requires_module('torchaudio._torchaudio') +def set_verbosity(verbosity: int): + """Set libsox's verbosity + + Args: + verbosity (int): Set verbosity level of libsox. + 1: failure messages + 2: warnings + 3: details of processing + 4-6: increasing levels of debug messages + + See Also: + http://sox.sourceforge.net/sox.html + """ + torch.ops.torchaudio.sox_utils_set_verbosity(verbosity) + + +@_mod_utils.requires_module('torchaudio._torchaudio') +def set_buffer_size(buffer_size: int): + """Set buffer size for sox effect chain + + Args: + buffer_size (int): Set the size in bytes of the buffers used for processing audio. + + See Also: + http://sox.sourceforge.net/sox.html + """ + torch.ops.torchaudio.sox_utils_set_buffer_size(buffer_size) + + +@_mod_utils.requires_module('torchaudio._torchaudio') +def set_use_threads(use_threads: bool): + """Set multithread option for sox effect chain + + Args: + use_threads (bool): When ``True``, enables ``libsox``'s parallel effects channels processing. + To use mutlithread, the underlying ``libsox`` has to be compiled with OpenMP support. + + See Also: + http://sox.sourceforge.net/sox.html + """ + torch.ops.torchaudio.sox_utils_set_use_threads(use_threads) + + +@_mod_utils.requires_module('torchaudio._torchaudio') +def list_effects() -> Dict[str, str]: + """List the available sox effect names + + Returns: + Dict[str, str]: Mapping from ``effect name`` to ``usage`` + """ + return dict(torch.ops.torchaudio.sox_utils_list_effects()) + + +@_mod_utils.requires_module('torchaudio._torchaudio') +def list_read_formats() -> List[str]: + """List the supported audio formats for read + + Returns: + List[str]: List of supported audio formats + """ + return torch.ops.torchaudio.sox_utils_list_read_formats() + + +@_mod_utils.requires_module('torchaudio._torchaudio') +def list_write_formats() -> List[str]: + """List the supported audio formats for write + + Returns: + List[str]: List of supported audio formats + """ + return torch.ops.torchaudio.sox_utils_list_write_formats() From 18be43cd7248272d5096c3695f34ebae26449193 Mon Sep 17 00:00:00 2001 From: Yanan Cao Date: Sat, 1 Aug 2020 13:02:20 -0700 Subject: [PATCH 14/19] Support custom exception message (#41907) Summary: Raise and assert used to have a hard-coded error message "Exception". User provided error message was ignored. This PR adds support to represent user's error message in TorchScript. This breaks backward compatibility because now we actually need to script the user's error message, which can potentially contain unscriptable expressions. Such programs can break when scripting, but saved models can still continue to work. Increased an op count in test_mobile_optimizer.py because now we need aten::format to form the actual exception message. This is built upon an WIP PR: https://github.com/pytorch/pytorch/pull/34112 by driazati Pull Request resolved: https://github.com/pytorch/pytorch/pull/41907 Reviewed By: ngimel Differential Revision: D22778301 Pulled By: gmagogsfm fbshipit-source-id: 2b94f0db4ae9fe70c4cd03f4048e519ea96323ad --- torchaudio/functional.py | 4 ++-- torchaudio/transforms.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/torchaudio/functional.py b/torchaudio/functional.py index 713544cc74..53795fef1a 100644 --- a/torchaudio/functional.py +++ b/torchaudio/functional.py @@ -223,8 +223,8 @@ def griffinlim( Returns: torch.Tensor: waveform of (..., time), where time equals the ``length`` parameter if given. """ - assert momentum < 1, 'momentum=%s > 1 can be unstable' % momentum - assert momentum >= 0, 'momentum=%s < 0' % momentum + assert momentum < 1, 'momentum={} > 1 can be unstable'.format(momentum) + assert momentum >= 0, 'momentum={} < 0'.format(momentum) # pack batch shape = specgram.size() diff --git a/torchaudio/transforms.py b/torchaudio/transforms.py index 72e55c94c8..ea0d6db492 100644 --- a/torchaudio/transforms.py +++ b/torchaudio/transforms.py @@ -607,7 +607,7 @@ def forward(self, waveform: Tensor) -> Tensor: return waveform - raise ValueError('Invalid resampling method: %s' % (self.resampling_method)) + raise ValueError('Invalid resampling method: ' + str(self.resampling_method)) class ComplexNorm(torch.nn.Module): From 355c1fdbda46a9fffc74ca1bbc061eccc62cc2c1 Mon Sep 17 00:00:00 2001 From: Moto Hira Date: Wed, 5 Aug 2020 11:03:05 -0700 Subject: [PATCH 15/19] Import torchaudio 20200804 Summary: Up to #804 Reviewed By: vincentqb Differential Revision: D22947671 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.../kaldi_io_test.py} | 2 +- .../librosa_compatibility_test.py} | 2 +- .../models_test.py} | 28 +- .../sox_compatibility_test.py} | 2 +- .../sox_effect}/__init__.py | 0 .../sox_effect/common.py | 2 +- .../sox_effect/dataset_test.py} | 2 +- .../sox_effect/smoke_test.py | 2 +- .../sox_effect/sox_effect_test.py} | 2 +- .../sox_effect/sox_effects_chain_test.py | 0 .../sox_effect/torchscript_test.py} | 2 +- .../sox_io_backend}/__init__.py | 0 .../sox_io_backend/common.py | 0 .../sox_io_backend/info_test.py} | 2 +- .../sox_io_backend/load_test.py} | 2 +- .../sox_io_backend/roundtrip_test.py} | 2 +- .../sox_io_backend/save_test.py} | 2 +- .../sox_io_backend/smoke_test.py | 2 +- .../sox_io_backend/torchscript_test.py} | 2 +- .../torchscript_consistency_cpu_test.py | 2 +- .../torchscript_consistency_cuda_test.py | 2 +- .../torchscript_consistency_impl.py | 2 +- .../transforms_test.py} | 2 +- test/torchaudio_unittest/utils/__init__.py | 0 .../utils/sox_utils_test.py} | 2 +- torchaudio/compliance/kaldi.py | 12 +- torchaudio/datasets/ljspeech.py | 2 +- torchaudio/functional.py | 98 +---- torchaudio/models/__init__.py | 2 +- torchaudio/models/{_wavernn.py => wavernn.py} | 64 +-- torchaudio/transforms.py | 10 +- 146 files changed, 878 insertions(+), 764 deletions(-) delete mode 100644 test/assets/ARCTIC/cmu_us_aew_arctic/etc/txt.done.data delete mode 100644 test/assets/ARCTIC/cmu_us_aew_arctic/wav/arctic_a0024.wav delete mode 100644 test/assets/LJSpeech-1.1/metadata.csv delete mode 100644 test/assets/LJSpeech-1.1/wavs/LJ001-0001.wav delete mode 100644 test/assets/LibriSpeech/dev-clean/1272/128104/1272-128104-0000.flac delete mode 100644 test/assets/LibriSpeech/dev-clean/1272/128104/1272-128104.trans.txt delete mode 100644 test/assets/SpeechCommands/speech_commands_v0.02/go/0a9f9af7_nohash_0.wav delete mode 100644 test/common_utils/backend_utils.py delete mode 100644 test/datasets/datasets_test.py delete mode 100644 test/functional_cpu_test.py rename test/{ => 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test/{sox_io_backend/test_torchscript.py => torchaudio_unittest/sox_io_backend/torchscript_test.py} (99%) rename test/{ => torchaudio_unittest}/torchscript_consistency_cpu_test.py (93%) rename test/{ => torchaudio_unittest}/torchscript_consistency_cuda_test.py (94%) rename test/{ => torchaudio_unittest}/torchscript_consistency_impl.py (99%) rename test/{test_transforms.py => torchaudio_unittest/transforms_test.py} (99%) create mode 100644 test/torchaudio_unittest/utils/__init__.py rename test/{utils/test_sox_utils.py => torchaudio_unittest/utils/sox_utils_test.py} (97%) rename torchaudio/models/{_wavernn.py => wavernn.py} (83%) diff --git a/.circleci/unittest/linux/scripts/environment.yml b/.circleci/unittest/linux/scripts/environment.yml index 108d97d193..121478165e 100644 --- a/.circleci/unittest/linux/scripts/environment.yml +++ b/.circleci/unittest/linux/scripts/environment.yml @@ -6,6 +6,7 @@ dependencies: - numpy - pytest - pytest-cov + - pytest-xdist - codecov - librosa - llvmlite==0.31 # See https://github.com/pytorch/audio/pull/766 diff --git a/.circleci/unittest/linux/scripts/install.sh b/.circleci/unittest/linux/scripts/install.sh index 131c9e43bf..8346e6c825 100755 --- a/.circleci/unittest/linux/scripts/install.sh +++ b/.circleci/unittest/linux/scripts/install.sh @@ -20,4 +20,4 @@ printf "Installing PyTorch with %s\n" "${cudatoolkit}" conda install -y -c pytorch-nightly pytorch "${cudatoolkit}" printf "* Installing torchaudio\n" -BUILD_SOX=1 python setup.py develop +BUILD_SOX=1 python setup.py install diff --git a/.circleci/unittest/linux/scripts/run_test.sh b/.circleci/unittest/linux/scripts/run_test.sh index 9ea942851e..50bcf711d6 100755 --- a/.circleci/unittest/linux/scripts/run_test.sh +++ b/.circleci/unittest/linux/scripts/run_test.sh @@ -2,9 +2,28 @@ set -e +case "$(uname -s)" in + Darwin*) os=MacOSX;; + *) os=Linux +esac + + eval "$(./conda/bin/conda shell.bash hook)" conda activate ./env python -m torch.utils.collect_env +export TORCHAUDIO_TEST_FAIL_IF_NO_EXTENSION=1 export PATH="${PWD}/third_party/install/bin/:${PATH}" -pytest --cov=torchaudio --junitxml=test-results/junit.xml -v --durations 20 test + +declare -a common_args=( + '--cov=torchaudio' + "--junitxml=${PWD}/test-results/junit.xml" + '--durations' '20' +) +if [ "${os}" == MacOSX ] ; then + declare -a args=('-q' '-n' 'auto' '--dist=loadscope') +else + declare -a args=('-v') +fi +cd test +pytest "${args[@]}" "${common_args[@]}" torchaudio_unittest diff --git a/.circleci/unittest/windows/scripts/install.sh b/.circleci/unittest/windows/scripts/install.sh index acbd79ae50..2b9c88b4af 100644 --- a/.circleci/unittest/windows/scripts/install.sh +++ b/.circleci/unittest/windows/scripts/install.sh @@ -20,4 +20,4 @@ printf "Installing PyTorch with %s\n" "${cudatoolkit}" conda install -y -c pytorch-nightly pytorch "${cudatoolkit}" printf "* Installing torchaudio\n" -python setup.py develop +python setup.py install diff --git a/.circleci/unittest/windows/scripts/run_test.sh b/.circleci/unittest/windows/scripts/run_test.sh index 252c1e9f46..7fdd1d4c6d 100644 --- a/.circleci/unittest/windows/scripts/run_test.sh +++ b/.circleci/unittest/windows/scripts/run_test.sh @@ -6,5 +6,5 @@ eval "$(./conda/Scripts/conda.exe 'shell.bash' 'hook')" conda activate ./env python -m torch.utils.collect_env -pytest --cov=torchaudio --junitxml=test-results/junit.xml -v --durations 20 test -flake8 torchaudio test +cd test +pytest --cov=torchaudio --junitxml=../test-results/junit.xml -v --durations 20 torchaudio_unittest diff --git a/README.md b/README.md index 290bb73543..70f30be243 100644 --- a/README.md +++ b/README.md @@ -37,6 +37,7 @@ The following is the corresponding ``torchaudio`` versions and supported Python | ``torch`` | ``torchaudio`` | ``python`` | | ------------------------ | ------------------------ | ------------------------------- | | ``master`` / ``nightly`` | ``master`` / ``nightly`` | ``>=3.6`` | +| ``1.6.0`` | ``0.6.0`` | ``>=3.6`` | | ``1.5.0`` | ``0.5.0`` | ``>=3.5`` | | ``1.4.0`` | ``0.4.0`` | ``==2.7``, ``>=3.5``, ``<=3.8`` | diff --git a/docs/source/functional.rst b/docs/source/functional.rst index 12ee165060..89e992bb89 100644 --- a/docs/source/functional.rst +++ b/docs/source/functional.rst @@ -8,11 +8,6 @@ torchaudio.functional Functions to perform common audio operations. -:hidden:`istft` -~~~~~~~~~~~~~~~ - -.. autofunction:: istft - :hidden:`spectrogram` ~~~~~~~~~~~~~~~~~~~~~ diff --git a/docs/source/models.rst b/docs/source/models.rst index 5b40482976..44eb74444a 100644 --- a/docs/source/models.rst +++ b/docs/source/models.rst @@ -15,3 +15,11 @@ The models subpackage contains definitions of models for addressing common audio .. autoclass:: Wav2Letter .. automethod:: forward + + +:hidden:`WaveRNN` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: WaveRNN + + .. automethod:: forward diff --git a/examples/pipeline_wavernn/datasets.py b/examples/pipeline_wavernn/datasets.py index 8d3068a229..58c2853f3d 100644 --- a/examples/pipeline_wavernn/datasets.py +++ b/examples/pipeline_wavernn/datasets.py @@ -4,7 +4,7 @@ import torch import torchaudio from torch.utils.data.dataset import random_split -from torchaudio.datasets import LJSPEECH +from torchaudio.datasets import LJSPEECH, LIBRITTS from torchaudio.transforms import MuLawEncoding from processing import bits_to_normalized_waveform, normalized_waveform_to_bits @@ -48,12 +48,20 @@ def process_datapoint(self, item): return item[0].squeeze(0), specgram -def split_process_ljspeech(args, transforms): - data = LJSPEECH(root=args.file_path, download=False) +def split_process_dataset(args, transforms): + if args.dataset == 'ljspeech': + data = LJSPEECH(root=args.file_path, download=False) - val_length = int(len(data) * args.val_ratio) - lengths = [len(data) - val_length, val_length] - train_dataset, val_dataset = random_split(data, lengths) + val_length = int(len(data) * args.val_ratio) + lengths = [len(data) - val_length, val_length] + train_dataset, val_dataset = random_split(data, lengths) + + elif args.dataset == 'libritts': + train_dataset = LIBRITTS(root=args.file_path, url='train-clean-100', download=False) + val_dataset = LIBRITTS(root=args.file_path, url='dev-clean', download=False) + + else: + raise ValueError(f"Expected dataset: `ljspeech` or `libritts`, but found {args.dataset}") train_dataset = Processed(train_dataset, transforms) val_dataset = Processed(val_dataset, transforms) diff --git a/examples/pipeline_wavernn/main.py b/examples/pipeline_wavernn/main.py index 032e9fc70e..9620dd5d45 100644 --- a/examples/pipeline_wavernn/main.py +++ b/examples/pipeline_wavernn/main.py @@ -13,9 +13,9 @@ from torch.optim import Adam from torch.utils.data import DataLoader from torchaudio.datasets.utils import bg_iterator -from torchaudio.models._wavernn import _WaveRNN +from torchaudio.models.wavernn import WaveRNN -from datasets import collate_factory, split_process_ljspeech +from datasets import collate_factory, split_process_dataset from losses import LongCrossEntropyLoss, MoLLoss from processing import LinearToMel, NormalizeDB from utils import MetricLogger, count_parameters, save_checkpoint @@ -55,6 +55,13 @@ def parse_args(): metavar="N", help="print frequency in epochs", ) + parser.add_argument( + "--dataset", + default="ljspeech", + choices=["ljspeech", "libritts"], + type=str, + help="select dataset to train with", + ) parser.add_argument( "--batch-size", default=256, type=int, metavar="N", help="mini-batch size" ) @@ -269,7 +276,7 @@ def main(args): NormalizeDB(min_level_db=args.min_level_db), ) - train_dataset, val_dataset = split_process_ljspeech(args, transforms) + train_dataset, val_dataset = split_process_dataset(args, transforms) loader_training_params = { "num_workers": args.workers, @@ -297,7 +304,7 @@ def main(args): n_classes = 2 ** args.n_bits if args.loss == "crossentropy" else 30 - model = _WaveRNN( + model = WaveRNN( upsample_scales=args.upsample_scales, n_classes=n_classes, hop_length=args.hop_length, diff --git a/setup.py b/setup.py index aab59b4d8a..0773b23107 100644 --- a/setup.py +++ b/setup.py @@ -3,8 +3,8 @@ import shutil import subprocess from pathlib import Path -import distutils.command.clean from setuptools import setup, find_packages +import distutils.command.clean from build_tools import setup_helpers @@ -85,5 +85,6 @@ def run(self): cmdclass={ 'build_ext': setup_helpers.BuildExtension.with_options(no_python_abi_suffix=True) }, - install_requires=[pytorch_package_dep] + install_requires=[pytorch_package_dep], + zip_safe=False, ) diff --git a/test/README.md b/test/README.md index c0a4b2a21d..629e5b54be 100644 --- a/test/README.md +++ b/test/README.md @@ -1,5 +1,27 @@ # Torchaudio Test Suite +## How to run test + +You can use `pytest` to run `torchaudio`'s test suites. See https://docs.pytest.org/ for the detail of how to use `pytest` command. + +``` +# List up all the tests +pytest test --collect-only +# Run all the test suites +pytest test +# Run tests on sox_effects module +pytest test/sox_effect +# use -k to apply filter +pytest test/sox_io_backend -k load # only runs tests where their names contain load +# Some other useful options; +# Stop on the first failure -x +# Run failure fast --ff +# Only rerun the failure --lf +``` + +**Note** +We use PyTorch's test utilities instead of `pytest` frameworks when writing tests to avoid reinventing the wheel for Tensor comparison. + ## Structure of tests The following is an overview of the tests and related modules for `torchaudio`. @@ -7,39 +29,39 @@ The following is an overview of the tests and related modules for `torchaudio`. ### Purpose specific test suites #### Numerical compatibility agains existing software -- [Librosa compatibility test](./test_librosa_compatibility.py) +- [Librosa compatibility test](./torchaudio_unittest/librosa_compatibility_test.py) Test suite for numerical compatibility against librosa. -- [SoX compatibility test](./test_sox_compatibility.py) +- [SoX compatibility test](./torchaudio_unittest/sox_compatibility_test.py) Test suite for numerical compatibility against SoX. -- [Kaldi compatibility test](./test_kaldi_compatibility.py) +- [Kaldi compatibility test](./torchaudio_unittest/kaldi_compatibility_test.py) Test suite for numerical compatibility against Kaldi. #### Result consistency with PyTorch framework -- [TorchScript consistency test](./test_torchscript_consistency.py) +- [TorchScript consistency test](./torchaudio_unittest/torchscript_consistency_impl.py) Test suite to check 1. if an API is TorchScript-able, and 2. the results from Python and Torchscript match. -- [Batch consistency test](./test_batch_consistency.py) +- [Batch consistency test](./torchaudio_unittest/batch_consistency_test.py) Test suite to check if functionals/Transforms handle single sample input and batch input and return the same result. ### Module specific test suites The following test modules are defined for corresponding `torchaudio` module/functions. -- [`torchaudio.datasets`](./test_datasets.py) -- [`torchaudio.functional`](./test_functional.py) -- [`torchaudio.transforms`](./test_transforms.py) -- [`torchaudio.compliance.kaldi`](./test_compliance_kaldi.py) -- [`torchaudio.kaldi_io`](./test_kaldi_io.py) +- [`torchaudio.datasets`](./torchaudio_unittest/datasets) +- [`torchaudio.functional`](./torchaudio_unittest/functional) +- [`torchaudio.transforms`](./torchaudio_unittest/transforms_test.py) +- [`torchaudio.compliance.kaldi`](./torchaudio_unittest/compliance_kaldi_test.py) +- [`torchaudio.kaldi_io`](./torchaudio_unittest/kaldi_io_test.py) - [`torchaudio.sox_effects`](test/sox_effects) -- [`torchaudio.save`, `torchaudio.load`, `torchaudio.info`](test/test_io.py) +- [`torchaudio.save`, `torchaudio.load`, `torchaudio.info`](./torchaudio_unittest/io_test.py) ### Test modules that do not fall into the above categories -- [test_dataloader.py](./test_dataloader.py) +- [test_dataloader.py](./torchaudio_unittest/dataloader_test.py) Simple test for loading data and applying preprocessing. ### Support files -- [assets](./assets): Contain sample audio files. -- [assets/kaldi](./assets/kaldi): Contains Kaldi format matrix files used in [./test_compliance_kaldi.py](./test_compliance_kaldi.py). -- [compliance](./compliance): Scripts used to generate above Kaldi matrix files. +- [assets](./torchaudio_unittest/assets): Contain sample audio files. +- [assets/kaldi](./torchaudio_unittest/assets/kaldi): Contains Kaldi format matrix files used in [./torchaudio_unittest/test_compliance_kaldi.py](./torchaudio_unittest/test_compliance_kaldi.py). +- [compliance](./torchaudio_unittest/compliance): Scripts used to generate above Kaldi matrix files. ### Waveforms for Testing Purposes @@ -86,11 +108,7 @@ Files: Files: * `CommonVoice/cv-corpus-4-2019-12-10/tt/clips/common_voice_tt_00000000.wav` -* `LibriSpeech/dev-clean/1272/128104/1272-128104-0000.flac` -* `LJSpeech-1.1/wavs/LJ001-0001.wav` -* `SpeechCommands/speech_commands_v0.02/go/0a9f9af7_nohash_0.wav` * `VCTK-Corpus/wav48/p224/p224_002.wav` -* `waves_yesno/0_1_0_1_0_1_1_0.wav` * `vad-go-stereo-44100.wav` * `vad-go-mono-32000.wav` @@ -98,13 +116,13 @@ Files: The following is the current practice of torchaudio test suite. -1. Unless the tests are related to I/O, use synthetic data. [`common_utils`](./common_utils.py) has some data generator functions. +1. Unless the tests are related to I/O, use synthetic data. [`common_utils`](./torchaudio_unittest/common_utils) has some data generator functions. 1. When you add a new test case, use `common_utils.TorchaudioTestCase` as base class unless you are writing tests that are common to CPU / CUDA. - Set class memeber `dtype`, `device` and `backend` for the desired behavior. - If you do not set `backend` value in your test suite, then I/O functions will be unassigned and attempt to load/save file will fail. - For `backend` value, in addition to available backends, you can also provide the value "default" and backend will be picked automatically based on availability. -1. If you are writing tests that should pass on diffrent dtype/devices, write a common class inheriting `common_utils.TestBaseMixin`, then inherit `common_utils.PytorchTestCase` and define class attributes (`dtype` / `device` / `backend`) there. See [Torchscript consistency test implementation](./torchscript_consistency_impl.py) and test definitions for [CPU](./torchscript_consistency_cpu_test.py) and [CUDA](./torchscript_consistency_cuda_test.py) devices. -1. For numerically comparing Tensors, use `assertEqual` method from `common_utils.PytorchTestCase` class. This method has a better support for a wide variety of Tensor types. +1. If you are writing tests that should pass on diffrent dtype/devices, write a common class inheriting `common_utils.TestBaseMixin`, then inherit `common_utils.PytorchTestCase` and define class attributes (`dtype` / `device` / `backend`) there. See [Torchscript consistency test implementation](./torchaudio_unittest/torchscript_consistency_impl.py) and test definitions for [CPU](./torchaudio_unittest/torchscript_consistency_cpu_test.py) and [CUDA](./torchaudio_unittest/torchscript_consistency_cuda_test.py) devices. +1. For numerically comparing Tensors, use `assertEqual` method from torchaudio_unittest.common_utils.PytorchTestCase` class. This method has a better support for a wide variety of Tensor types. 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zidd``W?e7VkES9mcn;^{Ik@^AVxLF=E&mvHVKmI27wQG>{!Oq23gLUd2WMg}yq;gM zI2f79bQT;}4rfHF9jF-I$5o$>WU3xNwRqkPQO7}q1;LSpNrOZ4YDbn=-<$$Q9_)W3_=x%N{|2Cn z@vzn|(^7E!v3fPG`xIjVs`q9i*=)l!rV@$6M*9gq?dA4(t3{7v5#)g!C})Tu{?Ogv zzHqzoNrFnB>?1z#!jYETbn2XAj^BCSHP1N_FnBsv*ZH7ZhhZ;(a#%Yn;HJ${oibd# zR6n7?Fc+$E=Z4e8IJ(iup_}9#csAqV6s&_|Xn_^oPhQ8>0>#fV!1np@tp5ORNF6HT P-k4=vBg5&3AZY#vXap6N diff --git a/test/common_utils/backend_utils.py b/test/common_utils/backend_utils.py deleted file mode 100644 index beceb6cafb..0000000000 --- a/test/common_utils/backend_utils.py +++ /dev/null @@ -1,41 +0,0 @@ -import unittest - -import torchaudio - -from .import data_utils - - -BACKENDS = torchaudio.list_audio_backends() - - -def _filter_backends_with_mp3(backends): - # Filter out backends that do not support mp3 - test_filepath = data_utils.get_asset_path('steam-train-whistle-daniel_simon.mp3') - - def supports_mp3(backend): - torchaudio.set_audio_backend(backend) - try: - torchaudio.load(test_filepath) - return True - except (RuntimeError, ImportError): - return False - - return [backend for backend in backends if supports_mp3(backend)] - - -BACKENDS_MP3 = _filter_backends_with_mp3(BACKENDS) - - -def set_audio_backend(backend): - """Allow additional backend value, 'default'""" - if backend == 'default': - if 'sox_io' in BACKENDS: - be = 'sox_io' - elif 'soundfile' in BACKENDS: - be = 'soundfile' - else: - raise unittest.SkipTest('No default backend available') - else: - be = backend - - torchaudio.set_audio_backend(be) diff --git a/test/datasets/datasets_test.py b/test/datasets/datasets_test.py deleted file mode 100644 index b8a980f346..0000000000 --- a/test/datasets/datasets_test.py +++ /dev/null @@ -1,60 +0,0 @@ -from torchaudio.datasets.commonvoice import COMMONVOICE -from torchaudio.datasets.librispeech import LIBRISPEECH -from torchaudio.datasets.speechcommands import SPEECHCOMMANDS -from torchaudio.datasets.utils import diskcache_iterator, bg_iterator -from torchaudio.datasets.vctk import VCTK -from torchaudio.datasets.ljspeech import LJSPEECH -from torchaudio.datasets.cmuarctic import CMUARCTIC - -from ..common_utils import ( - TorchaudioTestCase, - get_asset_path, -) - - -class TestDatasets(TorchaudioTestCase): - backend = 'default' - path = get_asset_path() - - def test_vctk(self): - data = VCTK(self.path) - data[0] - - def test_librispeech(self): - data = LIBRISPEECH(self.path, "dev-clean") - data[0] - - def test_ljspeech(self): - data = LJSPEECH(self.path) - data[0] - - def test_speechcommands(self): - data = SPEECHCOMMANDS(self.path) - data[0] - - def test_cmuarctic(self): - data = CMUARCTIC(self.path) - data[0] - - -class TestCommonVoice(TorchaudioTestCase): - backend = 'default' - path = get_asset_path() - - def test_commonvoice(self): - data = COMMONVOICE(self.path, url="tatar") - data[0] - - def test_commonvoice_diskcache(self): - data = COMMONVOICE(self.path, url="tatar") - data = diskcache_iterator(data) - # Save - data[0] - # Load - data[0] - - def test_commonvoice_bg(self): - data = COMMONVOICE(self.path, url="tatar") - data = bg_iterator(data, 5) - for _ in data: - pass diff --git a/test/functional_cpu_test.py b/test/functional_cpu_test.py deleted file mode 100644 index b162df2ebc..0000000000 --- a/test/functional_cpu_test.py +++ /dev/null @@ -1,407 +0,0 @@ -import math -import unittest - -import torch -import torchaudio -import torchaudio.functional as F -from parameterized import parameterized -import pytest - -from . import common_utils -from .functional_impl import Lfilter - - -def random_float_tensor(seed, size, a=22695477, c=1, m=2 ** 32): - """ Generates random tensors given a seed and size - https://en.wikipedia.org/wiki/Linear_congruential_generator - X_{n + 1} = (a * X_n + c) % m - Using Borland C/C++ values - - The tensor will have values between [0,1) - Inputs: - seed (int): an int - size (Tuple[int]): the size of the output tensor - a (int): the multiplier constant to the generator - c (int): the additive constant to the generator - m (int): the modulus constant to the generator - """ - num_elements = 1 - for s in size: - num_elements *= s - - arr = [(a * seed + c) % m] - for i in range(num_elements - 1): - arr.append((a * arr[i] + c) % m) - - return torch.tensor(arr).float().view(size) / m - - -class TestLFilterFloat32(Lfilter, common_utils.PytorchTestCase): - dtype = torch.float32 - device = torch.device('cpu') - - -class TestLFilterFloat64(Lfilter, common_utils.PytorchTestCase): - dtype = torch.float64 - device = torch.device('cpu') - - -class TestComputeDeltas(common_utils.TorchaudioTestCase): - """Test suite for correctness of compute_deltas""" - def test_one_channel(self): - specgram = torch.tensor([[[1.0, 2.0, 3.0, 4.0]]]) - expected = torch.tensor([[[0.5, 1.0, 1.0, 0.5]]]) - computed = F.compute_deltas(specgram, win_length=3) - torch.testing.assert_allclose(computed, expected) - - def test_two_channels(self): - specgram = torch.tensor([[[1.0, 2.0, 3.0, 4.0], - [1.0, 2.0, 3.0, 4.0]]]) - expected = torch.tensor([[[0.5, 1.0, 1.0, 0.5], - [0.5, 1.0, 1.0, 0.5]]]) - computed = F.compute_deltas(specgram, win_length=3) - torch.testing.assert_allclose(computed, expected) - - -def _compare_estimate(sound, estimate, atol=1e-6, rtol=1e-8): - # trim sound for case when constructed signal is shorter than original - sound = sound[..., :estimate.size(-1)] - torch.testing.assert_allclose(estimate, sound, atol=atol, rtol=rtol) - - -def _test_istft_is_inverse_of_stft(kwargs): - # generates a random sound signal for each tril and then does the stft/istft - # operation to check whether we can reconstruct signal - for data_size in [(2, 20), (3, 15), (4, 10)]: - for i in range(100): - - sound = random_float_tensor(i, data_size) - - stft = torch.stft(sound, **kwargs) - estimate = torchaudio.functional.istft(stft, length=sound.size(1), **kwargs) - - _compare_estimate(sound, estimate) - - -class TestIstft(common_utils.TorchaudioTestCase): - """Test suite for correctness of istft with various input""" - number_of_trials = 100 - - def test_istft_is_inverse_of_stft1(self): - # hann_window, centered, normalized, onesided - kwargs1 = { - 'n_fft': 12, - 'hop_length': 4, - 'win_length': 12, - 'window': torch.hann_window(12), - 'center': True, - 'pad_mode': 'reflect', - 'normalized': True, - 'onesided': True, - } - _test_istft_is_inverse_of_stft(kwargs1) - - def test_istft_is_inverse_of_stft2(self): - # hann_window, centered, not normalized, not onesided - kwargs2 = { - 'n_fft': 12, - 'hop_length': 2, - 'win_length': 8, - 'window': torch.hann_window(8), - 'center': True, - 'pad_mode': 'reflect', - 'normalized': False, - 'onesided': False, - } - _test_istft_is_inverse_of_stft(kwargs2) - - def test_istft_is_inverse_of_stft3(self): - # hamming_window, centered, normalized, not onesided - kwargs3 = { - 'n_fft': 15, - 'hop_length': 3, - 'win_length': 11, - 'window': torch.hamming_window(11), - 'center': True, - 'pad_mode': 'constant', - 'normalized': True, - 'onesided': False, - } - _test_istft_is_inverse_of_stft(kwargs3) - - def test_istft_is_inverse_of_stft4(self): - # hamming_window, not centered, not normalized, onesided - # window same size as n_fft - kwargs4 = { - 'n_fft': 5, - 'hop_length': 2, - 'win_length': 5, - 'window': torch.hamming_window(5), - 'center': False, - 'pad_mode': 'constant', - 'normalized': False, - 'onesided': True, - } - _test_istft_is_inverse_of_stft(kwargs4) - - def test_istft_is_inverse_of_stft5(self): - # hamming_window, not centered, not normalized, not onesided - # window same size as n_fft - kwargs5 = { - 'n_fft': 3, - 'hop_length': 2, - 'win_length': 3, - 'window': torch.hamming_window(3), - 'center': False, - 'pad_mode': 'reflect', - 'normalized': False, - 'onesided': False, - } - _test_istft_is_inverse_of_stft(kwargs5) - - def test_istft_of_ones(self): - # stft = torch.stft(torch.ones(4), 4) - stft = torch.tensor([ - [[4., 0.], [4., 0.], [4., 0.], [4., 0.], [4., 0.]], - [[0., 0.], [0., 0.], [0., 0.], [0., 0.], [0., 0.]], - [[0., 0.], [0., 0.], [0., 0.], [0., 0.], [0., 0.]] - ]) - - estimate = torchaudio.functional.istft(stft, n_fft=4, length=4) - _compare_estimate(torch.ones(4), estimate) - - def test_istft_of_zeros(self): - # stft = torch.stft(torch.zeros(4), 4) - stft = torch.zeros((3, 5, 2)) - - estimate = torchaudio.functional.istft(stft, n_fft=4, length=4) - _compare_estimate(torch.zeros(4), estimate) - - def test_istft_requires_overlap_windows(self): - # the window is size 1 but it hops 20 so there is a gap which throw an error - stft = torch.zeros((3, 5, 2)) - self.assertRaises(RuntimeError, torchaudio.functional.istft, stft, n_fft=4, - hop_length=20, win_length=1, window=torch.ones(1)) - - def test_istft_requires_nola(self): - stft = torch.zeros((3, 5, 2)) - kwargs_ok = { - 'n_fft': 4, - 'win_length': 4, - 'window': torch.ones(4), - } - - kwargs_not_ok = { - 'n_fft': 4, - 'win_length': 4, - 'window': torch.zeros(4), - } - - # A window of ones meets NOLA but a window of zeros does not. This should - # throw an error. - torchaudio.functional.istft(stft, **kwargs_ok) - self.assertRaises(RuntimeError, torchaudio.functional.istft, stft, **kwargs_not_ok) - - def test_istft_requires_non_empty(self): - self.assertRaises(RuntimeError, torchaudio.functional.istft, torch.zeros((3, 0, 2)), 2) - self.assertRaises(RuntimeError, torchaudio.functional.istft, torch.zeros((0, 3, 2)), 2) - - def _test_istft_of_sine(self, amplitude, L, n): - # stft of amplitude*sin(2*pi/L*n*x) with the hop length and window size equaling L - x = torch.arange(2 * L + 1, dtype=torch.get_default_dtype()) - sound = amplitude * torch.sin(2 * math.pi / L * x * n) - # stft = torch.stft(sound, L, hop_length=L, win_length=L, - # window=torch.ones(L), center=False, normalized=False) - stft = torch.zeros((L // 2 + 1, 2, 2)) - stft_largest_val = (amplitude * L) / 2.0 - if n < stft.size(0): - stft[n, :, 1] = -stft_largest_val - - if 0 <= L - n < stft.size(0): - # symmetric about L // 2 - stft[L - n, :, 1] = stft_largest_val - - estimate = torchaudio.functional.istft(stft, L, hop_length=L, win_length=L, - window=torch.ones(L), center=False, normalized=False) - # There is a larger error due to the scaling of amplitude - _compare_estimate(sound, estimate, atol=1e-3) - - def test_istft_of_sine(self): - self._test_istft_of_sine(amplitude=123, L=5, n=1) - self._test_istft_of_sine(amplitude=150, L=5, n=2) - self._test_istft_of_sine(amplitude=111, L=5, n=3) - self._test_istft_of_sine(amplitude=160, L=7, n=4) - self._test_istft_of_sine(amplitude=145, L=8, n=5) - self._test_istft_of_sine(amplitude=80, L=9, n=6) - self._test_istft_of_sine(amplitude=99, L=10, n=7) - - def _test_linearity_of_istft(self, data_size, kwargs, atol=1e-6, rtol=1e-8): - for i in range(self.number_of_trials): - tensor1 = random_float_tensor(i, data_size) - tensor2 = random_float_tensor(i * 2, data_size) - a, b = torch.rand(2) - istft1 = torchaudio.functional.istft(tensor1, **kwargs) - istft2 = torchaudio.functional.istft(tensor2, **kwargs) - istft = a * istft1 + b * istft2 - estimate = torchaudio.functional.istft(a * tensor1 + b * tensor2, **kwargs) - _compare_estimate(istft, estimate, atol, rtol) - - def test_linearity_of_istft1(self): - # hann_window, centered, normalized, onesided - kwargs1 = { - 'n_fft': 12, - 'window': torch.hann_window(12), - 'center': True, - 'pad_mode': 'reflect', - 'normalized': True, - 'onesided': True, - } - data_size = (2, 7, 7, 2) - self._test_linearity_of_istft(data_size, kwargs1) - - def test_linearity_of_istft2(self): - # hann_window, centered, not normalized, not onesided - kwargs2 = { - 'n_fft': 12, - 'window': torch.hann_window(12), - 'center': True, - 'pad_mode': 'reflect', - 'normalized': False, - 'onesided': False, - } - data_size = (2, 12, 7, 2) - self._test_linearity_of_istft(data_size, kwargs2) - - def test_linearity_of_istft3(self): - # hamming_window, centered, normalized, not onesided - kwargs3 = { - 'n_fft': 12, - 'window': torch.hamming_window(12), - 'center': True, - 'pad_mode': 'constant', - 'normalized': True, - 'onesided': False, - } - data_size = (2, 12, 7, 2) - self._test_linearity_of_istft(data_size, kwargs3) - - def test_linearity_of_istft4(self): - # hamming_window, not centered, not normalized, onesided - kwargs4 = { - 'n_fft': 12, - 'window': torch.hamming_window(12), - 'center': False, - 'pad_mode': 'constant', - 'normalized': False, - 'onesided': True, - } - data_size = (2, 7, 3, 2) - self._test_linearity_of_istft(data_size, kwargs4, atol=1e-5, rtol=1e-8) - - -class TestDetectPitchFrequency(common_utils.TorchaudioTestCase): - @parameterized.expand([(100,), (440,)]) - def test_pitch(self, frequency): - sample_rate = 44100 - test_sine_waveform = common_utils.get_sinusoid( - frequency=frequency, sample_rate=sample_rate, duration=5, - ) - - freq = torchaudio.functional.detect_pitch_frequency(test_sine_waveform, sample_rate) - - threshold = 1 - s = ((freq - frequency).abs() > threshold).sum() - self.assertFalse(s) - - -class TestDB_to_amplitude(common_utils.TorchaudioTestCase): - def test_DB_to_amplitude(self): - # Make some noise - x = torch.rand(1000) - spectrogram = torchaudio.transforms.Spectrogram() - spec = spectrogram(x) - - amin = 1e-10 - ref = 1.0 - db_multiplier = math.log10(max(amin, ref)) - - # Waveform amplitude -> DB -> amplitude - multiplier = 20. - power = 0.5 - - db = F.amplitude_to_DB(torch.abs(x), multiplier, amin, db_multiplier, top_db=None) - x2 = F.DB_to_amplitude(db, ref, power) - - torch.testing.assert_allclose(x2, torch.abs(x), atol=5e-5, rtol=1e-5) - - # Spectrogram amplitude -> DB -> amplitude - db = F.amplitude_to_DB(spec, multiplier, amin, db_multiplier, top_db=None) - x2 = F.DB_to_amplitude(db, ref, power) - - torch.testing.assert_allclose(x2, spec, atol=5e-5, rtol=1e-5) - - # Waveform power -> DB -> power - multiplier = 10. - power = 1. - - db = F.amplitude_to_DB(x, multiplier, amin, db_multiplier, top_db=None) - x2 = F.DB_to_amplitude(db, ref, power) - - torch.testing.assert_allclose(x2, torch.abs(x), atol=5e-5, rtol=1e-5) - - # Spectrogram power -> DB -> power - db = F.amplitude_to_DB(spec, multiplier, amin, db_multiplier, top_db=None) - x2 = F.DB_to_amplitude(db, ref, power) - - torch.testing.assert_allclose(x2, spec, atol=5e-5, rtol=1e-5) - - -@pytest.mark.parametrize('complex_tensor', [ - torch.randn(1, 2, 1025, 400, 2), - torch.randn(1025, 400, 2) -]) -@pytest.mark.parametrize('power', [1, 2, 0.7]) -def test_complex_norm(complex_tensor, power): - expected_norm_tensor = complex_tensor.pow(2).sum(-1).pow(power / 2) - norm_tensor = F.complex_norm(complex_tensor, power) - - torch.testing.assert_allclose(norm_tensor, expected_norm_tensor, atol=1e-5, rtol=1e-5) - - -@pytest.mark.parametrize('specgram', [ - torch.randn(2, 1025, 400), - torch.randn(1, 201, 100) -]) -@pytest.mark.parametrize('mask_param', [100]) -@pytest.mark.parametrize('mask_value', [0., 30.]) -@pytest.mark.parametrize('axis', [1, 2]) -def test_mask_along_axis(specgram, mask_param, mask_value, axis): - - mask_specgram = F.mask_along_axis(specgram, mask_param, mask_value, axis) - - other_axis = 1 if axis == 2 else 2 - - masked_columns = (mask_specgram == mask_value).sum(other_axis) - num_masked_columns = (masked_columns == mask_specgram.size(other_axis)).sum() - num_masked_columns //= mask_specgram.size(0) - - assert mask_specgram.size() == specgram.size() - assert num_masked_columns < mask_param - - -@pytest.mark.parametrize('mask_param', [100]) -@pytest.mark.parametrize('mask_value', [0., 30.]) -@pytest.mark.parametrize('axis', [2, 3]) -def test_mask_along_axis_iid(mask_param, mask_value, axis): - torch.random.manual_seed(42) - specgrams = torch.randn(4, 2, 1025, 400) - - mask_specgrams = F.mask_along_axis_iid(specgrams, mask_param, mask_value, axis) - - other_axis = 2 if axis == 3 else 3 - - masked_columns = (mask_specgrams == mask_value).sum(other_axis) - num_masked_columns = (masked_columns == mask_specgrams.size(other_axis)).sum(-1) - - assert mask_specgrams.size() == specgrams.size() - assert (num_masked_columns < mask_param).sum() == num_masked_columns.numel() diff --git a/test/__init__.py b/test/torchaudio_unittest/__init__.py similarity index 100% rename from test/__init__.py rename to test/torchaudio_unittest/__init__.py diff --git a/test/assets/CommonVoice/cv-corpus-4-2019-12-10/tt/clips/common_voice_tt_00000000.wav b/test/torchaudio_unittest/assets/CommonVoice/cv-corpus-4-2019-12-10/tt/clips/common_voice_tt_00000000.wav similarity index 100% rename from test/assets/CommonVoice/cv-corpus-4-2019-12-10/tt/clips/common_voice_tt_00000000.wav rename to test/torchaudio_unittest/assets/CommonVoice/cv-corpus-4-2019-12-10/tt/clips/common_voice_tt_00000000.wav diff --git a/test/assets/CommonVoice/cv-corpus-4-2019-12-10/tt/train.tsv b/test/torchaudio_unittest/assets/CommonVoice/cv-corpus-4-2019-12-10/tt/train.tsv similarity index 100% rename from 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b/test/torchaudio_unittest/assets/vad-go-mono-32000.wav similarity index 100% rename from test/assets/vad-go-mono-32000.wav rename to test/torchaudio_unittest/assets/vad-go-mono-32000.wav diff --git a/test/assets/vad-go-stereo-44100.wav b/test/torchaudio_unittest/assets/vad-go-stereo-44100.wav similarity index 100% rename from test/assets/vad-go-stereo-44100.wav rename to test/torchaudio_unittest/assets/vad-go-stereo-44100.wav diff --git a/test/assets/vec_flt.ark b/test/torchaudio_unittest/assets/vec_flt.ark similarity index 100% rename from test/assets/vec_flt.ark rename to test/torchaudio_unittest/assets/vec_flt.ark diff --git a/test/assets/vec_int.ark b/test/torchaudio_unittest/assets/vec_int.ark similarity index 100% rename from test/assets/vec_int.ark rename to test/torchaudio_unittest/assets/vec_int.ark diff --git a/test/test_backend.py b/test/torchaudio_unittest/backend_test.py similarity index 96% rename from test/test_backend.py rename to test/torchaudio_unittest/backend_test.py index 6b67cb2898..d7a2078a31 100644 --- a/test/test_backend.py +++ b/test/torchaudio_unittest/backend_test.py @@ -1,6 +1,6 @@ import torchaudio -from . import common_utils +from torchaudio_unittest import common_utils class BackendSwitchMixin: diff --git a/test/test_batch_consistency.py b/test/torchaudio_unittest/batch_consistency_test.py similarity index 96% rename from test/test_batch_consistency.py rename to test/torchaudio_unittest/batch_consistency_test.py index e7040fdc0b..696b714df1 100644 --- a/test/test_batch_consistency.py +++ b/test/torchaudio_unittest/batch_consistency_test.py @@ -7,7 +7,7 @@ import torchaudio import torchaudio.functional as F -from . import common_utils +from torchaudio_unittest import common_utils class TestFunctional(common_utils.TorchaudioTestCase): @@ -59,14 +59,6 @@ def test_detect_pitch_frequency(self, frequency, sample_rate, n_channels): n_channels=n_channels, duration=5) self.assert_batch_consistencies(F.detect_pitch_frequency, waveform, sample_rate) - def test_istft(self): - stft = torch.tensor([ - [[4., 0.], [4., 0.], [4., 0.], [4., 0.], [4., 0.]], - [[0., 0.], [0., 0.], [0., 0.], [0., 0.], [0., 0.]], - [[0., 0.], [0., 0.], [0., 0.], [0., 0.], [0., 0.]] - ]) - self.assert_batch_consistencies(F.istft, stft, n_fft=4, length=4) - def test_contrast(self): waveform = torch.rand(2, 100) - 0.5 self.assert_batch_consistencies(F.contrast, waveform, enhancement_amount=80.) diff --git a/test/common_utils/__init__.py b/test/torchaudio_unittest/common_utils/__init__.py similarity index 94% rename from test/common_utils/__init__.py rename to test/torchaudio_unittest/common_utils/__init__.py index 841cd26713..105a054864 100644 --- a/test/common_utils/__init__.py +++ b/test/torchaudio_unittest/common_utils/__init__.py @@ -5,8 +5,6 @@ ) from .backend_utils import ( set_audio_backend, - BACKENDS, - BACKENDS_MP3, ) from .case_utils import ( TempDirMixin, diff --git a/test/torchaudio_unittest/common_utils/backend_utils.py b/test/torchaudio_unittest/common_utils/backend_utils.py new file mode 100644 index 0000000000..7702a3d6f3 --- /dev/null +++ b/test/torchaudio_unittest/common_utils/backend_utils.py @@ -0,0 +1,19 @@ +import unittest + +import torchaudio + + +def set_audio_backend(backend): + """Allow additional backend value, 'default'""" + backends = torchaudio.list_audio_backends() + if backend == 'default': + if 'sox_io' in backends: + be = 'sox_io' + elif 'soundfile' in backends: + be = 'soundfile' + else: + raise unittest.SkipTest('No default backend available') + else: + be = backend + + torchaudio.set_audio_backend(be) diff --git a/test/common_utils/case_utils.py b/test/torchaudio_unittest/common_utils/case_utils.py similarity index 85% rename from test/common_utils/case_utils.py rename to test/torchaudio_unittest/common_utils/case_utils.py index 9c89ef6891..2e0a17b5da 100644 --- a/test/common_utils/case_utils.py +++ b/test/torchaudio_unittest/common_utils/case_utils.py @@ -67,4 +67,11 @@ def skipIfNoModule(module, display_name=None): skipIfNoSoxBackend = unittest.skipIf( 'sox' not in torchaudio.list_audio_backends(), 'Sox backend not available') skipIfNoCuda = unittest.skipIf(not torch.cuda.is_available(), reason='CUDA not available') -skipIfNoExtension = skipIfNoModule('torchaudio._torchaudio', 'torchaudio C++ extension') + + +def skipIfNoExtension(test_item): + if is_module_available('torchaudio._torchaudio'): + return test_item + if 'TORCHAUDIO_TEST_FAIL_IF_NO_EXTENSION' in os.environ: + raise RuntimeError('torchaudio C++ extension is not available.') + return unittest.skip('torchaudio C++ extension is not available')(test_item) diff --git a/test/common_utils/data_utils.py b/test/torchaudio_unittest/common_utils/data_utils.py similarity index 100% rename from test/common_utils/data_utils.py rename to test/torchaudio_unittest/common_utils/data_utils.py diff --git a/test/common_utils/parameterized_utils.py b/test/torchaudio_unittest/common_utils/parameterized_utils.py similarity index 100% rename from test/common_utils/parameterized_utils.py rename to test/torchaudio_unittest/common_utils/parameterized_utils.py diff --git a/test/common_utils/sox_utils.py b/test/torchaudio_unittest/common_utils/sox_utils.py similarity index 100% rename from test/common_utils/sox_utils.py rename to test/torchaudio_unittest/common_utils/sox_utils.py diff --git a/test/common_utils/wav_utils.py b/test/torchaudio_unittest/common_utils/wav_utils.py similarity index 100% rename from test/common_utils/wav_utils.py rename to test/torchaudio_unittest/common_utils/wav_utils.py diff --git a/test/compliance/__init__.py b/test/torchaudio_unittest/compliance/__init__.py similarity index 100% rename from test/compliance/__init__.py rename to test/torchaudio_unittest/compliance/__init__.py diff --git a/test/compliance/generate_fbank_data.py b/test/torchaudio_unittest/compliance/generate_fbank_data.py similarity index 98% rename from test/compliance/generate_fbank_data.py rename to test/torchaudio_unittest/compliance/generate_fbank_data.py index 446ebea284..96a935d116 100644 --- a/test/compliance/generate_fbank_data.py +++ b/test/torchaudio_unittest/compliance/generate_fbank_data.py @@ -92,7 +92,7 @@ def decode(fn, sound_path, exe_path, scp_path, out_dir): 'round_to_power_of_two', 'snip_edges', 'subtract_mean', 'use_energy', 'use_log_fbank', 'use_power', 'vtln_high', 'vtln_low', 'vtln_warp', 'window_type'] fn_split = fn.split('-') - assert len(fn_split) == len(arr), ('Len mismatch: %d and %d' % (len(fn_split), len(arr))) + assert len(fn_split) == len(arr), ('Len mismatch: {} and {}'.format(len(fn_split), len(arr))) inputs = {arr[i]: utils.parse(fn_split[i]) for i in range(len(arr))} # print flags for C++ diff --git a/test/compliance/generate_test_stft_data.py b/test/torchaudio_unittest/compliance/generate_test_stft_data.py similarity index 100% rename from test/compliance/generate_test_stft_data.py rename to test/torchaudio_unittest/compliance/generate_test_stft_data.py diff --git a/test/compliance/utils.py b/test/torchaudio_unittest/compliance/utils.py similarity index 100% rename from test/compliance/utils.py rename to test/torchaudio_unittest/compliance/utils.py diff --git a/test/test_compliance_kaldi.py b/test/torchaudio_unittest/compliance_kaldi_test.py similarity index 98% rename from test/test_compliance_kaldi.py rename to test/torchaudio_unittest/compliance_kaldi_test.py index 450183d6b3..43e9865e2f 100644 --- a/test/test_compliance_kaldi.py +++ b/test/torchaudio_unittest/compliance_kaldi_test.py @@ -6,7 +6,7 @@ import torchaudio import torchaudio.compliance.kaldi as kaldi -from . import common_utils +from torchaudio_unittest import common_utils from .compliance import utils as compliance_utils @@ -148,7 +148,9 @@ def _compliance_test_helper(self, sound_filepath, filepath_key, expected_num_fil sound, sr = torchaudio.load_wav(sound_filepath) files = self.test_filepaths[filepath_key] - assert len(files) == expected_num_files, ('number of kaldi %s file changed to %d' % (filepath_key, len(files))) + assert len(files) == expected_num_files, \ + ('number of kaldi {} file changed to {}'.format( + filepath_key, len(files))) for f in files: print(f) diff --git a/test/test_dataloader.py b/test/torchaudio_unittest/dataloader_test.py similarity index 96% rename from test/test_dataloader.py rename to test/torchaudio_unittest/dataloader_test.py index 6d1518b70c..8f1d7e6fb3 100644 --- a/test/test_dataloader.py +++ b/test/torchaudio_unittest/dataloader_test.py @@ -3,7 +3,7 @@ import torchaudio from torch.utils.data import Dataset, DataLoader -from . import common_utils +from torchaudio_unittest import common_utils class TORCHAUDIODS(Dataset): diff --git a/test/datasets/__init__.py b/test/torchaudio_unittest/datasets/__init__.py similarity index 100% rename from test/datasets/__init__.py rename to test/torchaudio_unittest/datasets/__init__.py diff --git a/test/torchaudio_unittest/datasets/cmuarctic_test.py b/test/torchaudio_unittest/datasets/cmuarctic_test.py new file mode 100644 index 0000000000..578b0ec385 --- /dev/null +++ b/test/torchaudio_unittest/datasets/cmuarctic_test.py @@ -0,0 +1,67 @@ +import os + +from torchaudio.datasets import cmuarctic + +from torchaudio_unittest.common_utils import ( + TempDirMixin, + TorchaudioTestCase, + get_whitenoise, + save_wav, + normalize_wav, +) + + +class TestCMUARCTIC(TempDirMixin, TorchaudioTestCase): + backend = "default" + + root_dir = None + samples = [] + + @classmethod + def setUpClass(cls): + cls.root_dir = cls.get_base_temp_dir() + sample_rate = 16000 + utterance = "This is a test utterance." + + base_dir = os.path.join(cls.root_dir, "ARCTIC", "cmu_us_aew_arctic") + txt_dir = os.path.join(base_dir, "etc") + os.makedirs(txt_dir, exist_ok=True) + txt_file = os.path.join(txt_dir, "txt.done.data") + audio_dir = os.path.join(base_dir, "wav") + os.makedirs(audio_dir, exist_ok=True) + + seed = 42 + with open(txt_file, "w") as txt: + for c in ["a", "b"]: + for i in range(5): + utterance_id = f"arctic_{c}{i:04d}" + path = os.path.join(audio_dir, f"{utterance_id}.wav") + data = get_whitenoise( + sample_rate=sample_rate, + duration=3, + n_channels=1, + dtype="int16", + seed=seed, + ) + save_wav(path, data, sample_rate) + sample = ( + normalize_wav(data), + sample_rate, + utterance, + utterance_id.split("_")[1], + ) + cls.samples.append(sample) + txt.write(f'( {utterance_id} "{utterance}" )\n') + seed += 1 + + def test_cmuarctic(self): + dataset = cmuarctic.CMUARCTIC(self.root_dir) + n_ite = 0 + for i, (waveform, sample_rate, utterance, utterance_id) in enumerate(dataset): + expected_sample = self.samples[i] + assert sample_rate == expected_sample[1] + assert utterance == expected_sample[2] + assert utterance_id == expected_sample[3] + self.assertEqual(expected_sample[0], waveform, atol=5e-5, rtol=1e-8) + n_ite += 1 + assert n_ite == len(self.samples) diff --git a/test/torchaudio_unittest/datasets/commonvoice_test.py b/test/torchaudio_unittest/datasets/commonvoice_test.py new file mode 100644 index 0000000000..3eedebec70 --- /dev/null +++ b/test/torchaudio_unittest/datasets/commonvoice_test.py @@ -0,0 +1,72 @@ +import os +import csv +import random + +from torchaudio.datasets import commonvoice +from torchaudio_unittest.common_utils import ( + TempDirMixin, + TorchaudioTestCase, + get_whitenoise, + save_wav, + normalize_wav, +) + + +class TestCommonVoice(TempDirMixin, TorchaudioTestCase): + backend = 'default' + + root_dir = None + data = [] + _headers = [u"client_ids", u"path", u"sentence", u"up_votes", u"down_votes", u"age", u"gender", u"accent"] + # Note: extension is changed to wav for the sake of test + # Note: the first content is missing values for `age`, `gender` and `accent` as in the original data. + _train_csv_contents = [ + ["9d16c5d980247861130e0480e2719f448be73d86a496c36d01a477cbdecd8cfd1399403d7a77bf458d211a70711b2da0845c", + "common_voice_en_18885784.wav", + "He was accorded a State funeral, and was buried in Drayton and Toowoomba Cemetery.", "2", "0", "", "", ""], + ["c82eb9291328620f06025a1f8112b909099e447e485e99236cb87df008650250e79fea5ca772061fb6a370830847b9c44d20", + "common_voice_en_556542.wav", "Once more into the breach", "2", "0", "thirties", "male", "us"], + ["f74d880c5ad4c5917f314a604d3fc4805159d255796fb9f8defca35333ecc002bdf53dc463503c12674ea840b21b4a507b7c", + "common_voice_en_18607573.wav", + "Caddy, show Miss Clare and Miss Summerson their rooms.", "2", "0", "twenties", "male", "canada"], + ] + _folder_audio = "clips" + sample_rate = 48000 + + @classmethod + def setUpClass(cls): + cls.root_dir = cls.get_base_temp_dir() + # The path convention commonvoice uses + base_dir = os.path.join(cls.root_dir, commonvoice.FOLDER_IN_ARCHIVE, commonvoice.VERSION, "en") + os.makedirs(base_dir, exist_ok=True) + + # Tsv file name difference does not mean different subset, testing as a whole dataset here + tsv_filename = os.path.join(base_dir, commonvoice.TSV) + with open(tsv_filename, "w", newline='') as tsv: + writer = csv.writer(tsv, delimiter='\t') + writer.writerow(cls._headers) + for i, content in enumerate(cls._train_csv_contents): + audio_filename = audio_filename = content[1] + writer.writerow(content) + + # Generate and store audio + audio_base_path = os.path.join(base_dir, cls._folder_audio) + os.makedirs(audio_base_path, exist_ok=True) + audio_path = os.path.join(audio_base_path, audio_filename) + data = get_whitenoise(sample_rate=cls.sample_rate, duration=1, n_channels=1, seed=i, dtype='float32') + save_wav(audio_path, data, cls.sample_rate) + + # Append data entry + cls.data.append((normalize_wav(data), cls.sample_rate, dict(zip(cls._headers, content)))) + + def test_commonvoice(self): + dataset = commonvoice.COMMONVOICE(self.root_dir) + n_ite = 0 + for i, (waveform, sample_rate, dictionary) in enumerate(dataset): + expected_dictionary = self.data[i][2] + expected_data = self.data[i][0] + self.assertEqual(expected_data, waveform, atol=5e-5, rtol=1e-8) + assert sample_rate == TestCommonVoice.sample_rate + assert dictionary == expected_dictionary + n_ite += 1 + assert n_ite == len(self.data) diff --git a/test/torchaudio_unittest/datasets/datasets_test.py b/test/torchaudio_unittest/datasets/datasets_test.py new file mode 100644 index 0000000000..cd04c4d447 --- /dev/null +++ b/test/torchaudio_unittest/datasets/datasets_test.py @@ -0,0 +1,17 @@ +from torchaudio.datasets.librispeech import LIBRISPEECH +from torchaudio.datasets.speechcommands import SPEECHCOMMANDS +from torchaudio.datasets.vctk import VCTK + +from torchaudio_unittest.common_utils import ( + TorchaudioTestCase, + get_asset_path, +) + + +class TestDatasets(TorchaudioTestCase): + backend = 'default' + path = get_asset_path() + + def test_vctk(self): + data = VCTK(self.path) + data[0] diff --git a/test/datasets/gtzan_test.py b/test/torchaudio_unittest/datasets/gtzan_test.py similarity index 98% rename from test/datasets/gtzan_test.py rename to test/torchaudio_unittest/datasets/gtzan_test.py index 14c82a512a..3236535fad 100644 --- a/test/datasets/gtzan_test.py +++ b/test/torchaudio_unittest/datasets/gtzan_test.py @@ -2,7 +2,7 @@ from torchaudio.datasets import gtzan -from ..common_utils import ( +from torchaudio_unittest.common_utils import ( TempDirMixin, TorchaudioTestCase, get_whitenoise, diff --git a/test/torchaudio_unittest/datasets/librispeech_test.py b/test/torchaudio_unittest/datasets/librispeech_test.py new file mode 100644 index 0000000000..669b428876 --- /dev/null +++ b/test/torchaudio_unittest/datasets/librispeech_test.py @@ -0,0 +1,112 @@ +import os + +from torchaudio.datasets import librispeech + +from torchaudio_unittest.common_utils import ( + TempDirMixin, + TorchaudioTestCase, + get_whitenoise, + save_wav, + normalize_wav, +) + +# Used to generate a unique utterance for each dummy audio file +NUMBERS = [ + 'ZERO', + 'ONE', + 'TWO', + 'THREE', + 'FOUR', + 'FIVE', + 'SIX', + 'SEVEN', + 'EIGHT', + 'NINE' +] + + +class TestLibriSpeech(TempDirMixin, TorchaudioTestCase): + backend = 'default' + + root_dir = None + samples = [] + + @classmethod + def setUpClass(cls): + cls.root_dir = cls.get_base_temp_dir() + dataset_dir = os.path.join( + cls.root_dir, librispeech.FOLDER_IN_ARCHIVE, librispeech.URL + ) + os.makedirs(dataset_dir, exist_ok=True) + sample_rate = 16000 # 16kHz + seed = 0 + + for speaker_id in range(5): + speaker_path = os.path.join(dataset_dir, str(speaker_id)) + os.makedirs(speaker_path, exist_ok=True) + + for chapter_id in range(3): + chapter_path = os.path.join(speaker_path, str(chapter_id)) + os.makedirs(chapter_path, exist_ok=True) + trans_content = [] + + for utterance_id in range(10): + filename = f'{speaker_id}-{chapter_id}-{utterance_id:04d}.wav' + path = os.path.join(chapter_path, filename) + + utterance = ' '.join( + [NUMBERS[x] for x in [speaker_id, chapter_id, utterance_id]] + ) + trans_content.append( + f'{speaker_id}-{chapter_id}-{utterance_id:04d} {utterance}' + ) + + data = get_whitenoise( + sample_rate=sample_rate, + duration=0.01, + n_channels=1, + dtype='float32', + seed=seed + ) + save_wav(path, data, sample_rate) + sample = ( + normalize_wav(data), + sample_rate, + utterance, + speaker_id, + chapter_id, + utterance_id + ) + cls.samples.append(sample) + + seed += 1 + + trans_filename = f'{speaker_id}-{chapter_id}.trans.txt' + trans_path = os.path.join(chapter_path, trans_filename) + with open(trans_path, 'w') as f: + f.write('\n'.join(trans_content)) + + @classmethod + def tearDownClass(cls): + # In case of test failure + librispeech.LIBRISPEECH._ext_audio = '.flac' + + def test_librispeech(self): + librispeech.LIBRISPEECH._ext_audio = '.wav' + dataset = librispeech.LIBRISPEECH(self.root_dir) + print(dataset._path) + + num_samples = 0 + for i, ( + data, sample_rate, utterance, speaker_id, chapter_id, utterance_id + ) in enumerate(dataset): + self.assertEqual(data, self.samples[i][0], atol=5e-5, rtol=1e-8) + assert sample_rate == self.samples[i][1] + assert utterance == self.samples[i][2] + assert speaker_id == self.samples[i][3] + assert chapter_id == self.samples[i][4] + assert utterance_id == self.samples[i][5] + num_samples += 1 + + assert num_samples == len(self.samples) + librispeech.LIBRISPEECH._ext_audio = '.flac' diff --git a/test/datasets/libritts_test.py b/test/torchaudio_unittest/datasets/libritts_test.py similarity index 91% rename from test/datasets/libritts_test.py rename to test/torchaudio_unittest/datasets/libritts_test.py index f31561d211..639098446b 100644 --- a/test/datasets/libritts_test.py +++ b/test/torchaudio_unittest/datasets/libritts_test.py @@ -2,7 +2,7 @@ from torchaudio.datasets.libritts import LIBRITTS -from ..common_utils import ( +from torchaudio_unittest.common_utils import ( TempDirMixin, TorchaudioTestCase, get_whitenoise, @@ -33,8 +33,8 @@ def setUpClass(cls): os.makedirs(file_dir, exist_ok=True) path = os.path.join(file_dir, filename) - data = get_whitenoise(sample_rate=8000, duration=6, n_channels=1, dtype='int16', seed=i) - save_wav(path, data, 8000) + data = get_whitenoise(sample_rate=24000, duration=2, n_channels=1, dtype='int16', seed=i) + save_wav(path, data, 24000) cls.data.append(normalize_wav(data)) original_text_filename = f'{"_".join(str(u) for u in utterance_id)}.original.txt' @@ -61,7 +61,7 @@ def test_libritts(self): expected_ids = self.utterance_ids[i] expected_data = self.data[i] self.assertEqual(expected_data, waveform, atol=5e-5, rtol=1e-8) - assert sample_rate == 8000 + assert sample_rate == 24000 assert speaker_id == expected_ids[0] assert chapter_id == expected_ids[1] assert original_text == self.original_text diff --git a/test/torchaudio_unittest/datasets/ljspeech_test.py b/test/torchaudio_unittest/datasets/ljspeech_test.py new file mode 100644 index 0000000000..7c1128c31b --- /dev/null +++ b/test/torchaudio_unittest/datasets/ljspeech_test.py @@ -0,0 +1,74 @@ +import csv +import os + +from torchaudio.datasets import ljspeech + +from torchaudio_unittest.common_utils import ( + TempDirMixin, + TorchaudioTestCase, + get_whitenoise, + normalize_wav, + save_wav, +) + + +class TestLJSpeech(TempDirMixin, TorchaudioTestCase): + backend = "default" + + root_dir = None + data = [] + transcripts = [ + "Test transcript 1", + "Test transcript 2", + "Test transcript 3", + "In 1465 Sweynheim and Pannartz began printing in the monastery of Subiaco near Rome," + ] + + normalized_transcripts = [ + "Test transcript one", + "Test transcript two", + "Test transcript three", + "In fourteen sixty-five Sweynheim and Pannartz began printing in the monastery of Subiaco near Rome," + ] + + @classmethod + def setUpClass(cls): + cls.root_dir = cls.get_base_temp_dir() + base_dir = os.path.join(cls.root_dir, "LJSpeech-1.1") + archive_dir = os.path.join(base_dir, "wavs") + os.makedirs(archive_dir, exist_ok=True) + metadata_path = os.path.join(base_dir, "metadata.csv") + sample_rate = 22050 + + with open(metadata_path, mode="w", newline='') as metadata_file: + metadata_writer = csv.writer( + metadata_file, delimiter="|", quoting=csv.QUOTE_NONE + ) + for i, (transcript, normalized_transcript) in enumerate( + zip(cls.transcripts, cls.normalized_transcripts) + ): + fileid = f'LJ001-{i:04d}' + metadata_writer.writerow([fileid, transcript, normalized_transcript]) + filename = fileid + ".wav" + path = os.path.join(archive_dir, filename) + data = get_whitenoise( + sample_rate=sample_rate, duration=1, n_channels=1, dtype="int16", seed=i + ) + save_wav(path, data, sample_rate) + cls.data.append(normalize_wav(data)) + + def test_ljspeech(self): + dataset = ljspeech.LJSPEECH(self.root_dir) + n_ite = 0 + for i, (waveform, sample_rate, transcript, normalized_transcript) in enumerate( + dataset + ): + expected_transcript = self.transcripts[i] + expected_normalized_transcript = self.normalized_transcripts[i] + expected_data = self.data[i] + self.assertEqual(expected_data, waveform, atol=5e-5, rtol=1e-8) + assert sample_rate == sample_rate + assert transcript == expected_transcript + assert normalized_transcript == expected_normalized_transcript + n_ite += 1 + assert n_ite == len(self.data) diff --git a/test/torchaudio_unittest/datasets/speechcommands_test.py b/test/torchaudio_unittest/datasets/speechcommands_test.py new file mode 100644 index 0000000000..7df1ea2007 --- /dev/null +++ b/test/torchaudio_unittest/datasets/speechcommands_test.py @@ -0,0 +1,109 @@ +import os + +from torchaudio.datasets import speechcommands + +from torchaudio_unittest.common_utils import ( + TempDirMixin, + TorchaudioTestCase, + get_whitenoise, + normalize_wav, + save_wav, +) + +LABELS = [ + "bed", + "bird", + "cat", + "dog", + "down", + "eight", + "five", + "follow", + "forward", + "four", + "go", + "happy", + "house", + "learn", + "left", + "marvin", + "nine", + "no", + "off", + "on", + "one", + "right", + "seven", + "sheila", + "six", + "stop", + "three", + "tree", + "two", + "up", + "visual", + "wow", + "yes", + "zero", +] + + +class TestSpeechCommands(TempDirMixin, TorchaudioTestCase): + backend = "default" + + root_dir = None + samples = [] + + @classmethod + def setUp(cls): + cls.root_dir = cls.get_base_temp_dir() + dataset_dir = os.path.join( + cls.root_dir, speechcommands.FOLDER_IN_ARCHIVE, speechcommands.URL + ) + os.makedirs(dataset_dir, exist_ok=True) + sample_rate = 16000 # 16kHz sample rate + seed = 0 + for label in LABELS: + path = os.path.join(dataset_dir, label) + os.makedirs(path, exist_ok=True) + for j in range(2): + # generate hash ID for speaker + speaker = "{:08x}".format(j) + + for utterance in range(3): + filename = f"{speaker}{speechcommands.HASH_DIVIDER}{utterance}.wav" + file_path = os.path.join(path, filename) + seed += 1 + data = get_whitenoise( + sample_rate=sample_rate, + duration=0.01, + n_channels=1, + dtype="int16", + seed=seed, + ) + save_wav(file_path, data, sample_rate) + sample = ( + normalize_wav(data), + sample_rate, + label, + speaker, + utterance, + ) + cls.samples.append(sample) + + def testSpeechCommands(self): + dataset = speechcommands.SPEECHCOMMANDS(self.root_dir) + print(dataset._path) + + num_samples = 0 + for i, (data, sample_rate, label, speaker_id, utterance_number) in enumerate( + dataset + ): + self.assertEqual(data, self.samples[i][0], atol=5e-5, rtol=1e-8) + assert sample_rate == self.samples[i][1] + assert label == self.samples[i][2] + assert speaker_id == self.samples[i][3] + assert utterance_number == self.samples[i][4] + num_samples += 1 + + assert num_samples == len(self.samples) diff --git a/test/datasets/utils_test.py b/test/torchaudio_unittest/datasets/utils_test.py similarity index 68% rename from test/datasets/utils_test.py rename to test/torchaudio_unittest/datasets/utils_test.py index e77df09f4c..f75ae2319c 100644 --- a/test/datasets/utils_test.py +++ b/test/torchaudio_unittest/datasets/utils_test.py @@ -2,10 +2,12 @@ from pathlib import Path from torchaudio.datasets import utils as dataset_utils +from torchaudio.datasets.commonvoice import COMMONVOICE -from ..common_utils import ( +from torchaudio_unittest.common_utils import ( TempDirMixin, TorchaudioTestCase, + get_asset_path, ) @@ -45,3 +47,22 @@ def test_walk_files(self): assert found == self.expected[i] n_ites += 1 assert n_ites == len(self.expected) + + +class TestIterator(TorchaudioTestCase): + backend = 'default' + path = get_asset_path() + + def test_disckcache_iterator(self): + data = COMMONVOICE(self.path, url="tatar") + data = dataset_utils.diskcache_iterator(data) + # Save + data[0] + # Load + data[0] + + def test_bg_iterator(self): + data = COMMONVOICE(self.path, url="tatar") + data = dataset_utils.bg_iterator(data, 5) + for _ in data: + pass diff --git a/test/datasets/yesno_test.py b/test/torchaudio_unittest/datasets/yesno_test.py similarity index 91% rename from test/datasets/yesno_test.py rename to test/torchaudio_unittest/datasets/yesno_test.py index f9c4d0b051..a8a8a04276 100644 --- a/test/datasets/yesno_test.py +++ b/test/torchaudio_unittest/datasets/yesno_test.py @@ -2,7 +2,7 @@ from torchaudio.datasets import yesno -from ..common_utils import ( +from torchaudio_unittest.common_utils import ( TempDirMixin, TorchaudioTestCase, get_whitenoise, @@ -29,10 +29,10 @@ def setUpClass(cls): cls.root_dir = cls.get_base_temp_dir() base_dir = os.path.join(cls.root_dir, 'waves_yesno') os.makedirs(base_dir, exist_ok=True) - for label in cls.labels: + for i, label in enumerate(cls.labels): filename = f'{"_".join(str(l) for l in label)}.wav' path = os.path.join(base_dir, filename) - data = get_whitenoise(sample_rate=8000, duration=6, n_channels=1, dtype='int16') + data = get_whitenoise(sample_rate=8000, duration=6, n_channels=1, dtype='int16', seed=i) save_wav(path, data, 8000) cls.data.append(normalize_wav(data)) diff --git a/test/sox_effect/__init__.py b/test/torchaudio_unittest/functional/__init__.py similarity index 100% rename from test/sox_effect/__init__.py rename to test/torchaudio_unittest/functional/__init__.py diff --git a/test/torchaudio_unittest/functional/functional_cpu_test.py b/test/torchaudio_unittest/functional/functional_cpu_test.py new file mode 100644 index 0000000000..96aac4e770 --- /dev/null +++ b/test/torchaudio_unittest/functional/functional_cpu_test.py @@ -0,0 +1,146 @@ +import math +import unittest + +import torch +import torchaudio +import torchaudio.functional as F +from parameterized import parameterized +import pytest + +from torchaudio_unittest import common_utils +from .functional_impl import Lfilter + + +class TestLFilterFloat32(Lfilter, common_utils.PytorchTestCase): + dtype = torch.float32 + device = torch.device('cpu') + + +class TestLFilterFloat64(Lfilter, common_utils.PytorchTestCase): + dtype = torch.float64 + device = torch.device('cpu') + + +class TestComputeDeltas(common_utils.TorchaudioTestCase): + """Test suite for correctness of compute_deltas""" + def test_one_channel(self): + specgram = torch.tensor([[[1.0, 2.0, 3.0, 4.0]]]) + expected = torch.tensor([[[0.5, 1.0, 1.0, 0.5]]]) + computed = F.compute_deltas(specgram, win_length=3) + torch.testing.assert_allclose(computed, expected) + + def test_two_channels(self): + specgram = torch.tensor([[[1.0, 2.0, 3.0, 4.0], + [1.0, 2.0, 3.0, 4.0]]]) + expected = torch.tensor([[[0.5, 1.0, 1.0, 0.5], + [0.5, 1.0, 1.0, 0.5]]]) + computed = F.compute_deltas(specgram, win_length=3) + torch.testing.assert_allclose(computed, expected) + + +class TestDetectPitchFrequency(common_utils.TorchaudioTestCase): + @parameterized.expand([(100,), (440,)]) + def test_pitch(self, frequency): + sample_rate = 44100 + test_sine_waveform = common_utils.get_sinusoid( + frequency=frequency, sample_rate=sample_rate, duration=5, + ) + + freq = torchaudio.functional.detect_pitch_frequency(test_sine_waveform, sample_rate) + + threshold = 1 + s = ((freq - frequency).abs() > threshold).sum() + self.assertFalse(s) + + +class TestDB_to_amplitude(common_utils.TorchaudioTestCase): + def test_DB_to_amplitude(self): + # Make some noise + x = torch.rand(1000) + spectrogram = torchaudio.transforms.Spectrogram() + spec = spectrogram(x) + + amin = 1e-10 + ref = 1.0 + db_multiplier = math.log10(max(amin, ref)) + + # Waveform amplitude -> DB -> amplitude + multiplier = 20. + power = 0.5 + + db = F.amplitude_to_DB(torch.abs(x), multiplier, amin, db_multiplier, top_db=None) + x2 = F.DB_to_amplitude(db, ref, power) + + torch.testing.assert_allclose(x2, torch.abs(x), atol=5e-5, rtol=1e-5) + + # Spectrogram amplitude -> DB -> amplitude + db = F.amplitude_to_DB(spec, multiplier, amin, db_multiplier, top_db=None) + x2 = F.DB_to_amplitude(db, ref, power) + + torch.testing.assert_allclose(x2, spec, atol=5e-5, rtol=1e-5) + + # Waveform power -> DB -> power + multiplier = 10. + power = 1. + + db = F.amplitude_to_DB(x, multiplier, amin, db_multiplier, top_db=None) + x2 = F.DB_to_amplitude(db, ref, power) + + torch.testing.assert_allclose(x2, torch.abs(x), atol=5e-5, rtol=1e-5) + + # Spectrogram power -> DB -> power + db = F.amplitude_to_DB(spec, multiplier, amin, db_multiplier, top_db=None) + x2 = F.DB_to_amplitude(db, ref, power) + + torch.testing.assert_allclose(x2, spec, atol=5e-5, rtol=1e-5) + + +@pytest.mark.parametrize('complex_tensor', [ + torch.randn(1, 2, 1025, 400, 2), + torch.randn(1025, 400, 2) +]) +@pytest.mark.parametrize('power', [1, 2, 0.7]) +def test_complex_norm(complex_tensor, power): + expected_norm_tensor = complex_tensor.pow(2).sum(-1).pow(power / 2) + norm_tensor = F.complex_norm(complex_tensor, power) + + torch.testing.assert_allclose(norm_tensor, expected_norm_tensor, atol=1e-5, rtol=1e-5) + + +@pytest.mark.parametrize('specgram', [ + torch.randn(2, 1025, 400), + torch.randn(1, 201, 100) +]) +@pytest.mark.parametrize('mask_param', [100]) +@pytest.mark.parametrize('mask_value', [0., 30.]) +@pytest.mark.parametrize('axis', [1, 2]) +def test_mask_along_axis(specgram, mask_param, mask_value, axis): + + mask_specgram = F.mask_along_axis(specgram, mask_param, mask_value, axis) + + other_axis = 1 if axis == 2 else 2 + + masked_columns = (mask_specgram == mask_value).sum(other_axis) + num_masked_columns = (masked_columns == mask_specgram.size(other_axis)).sum() + num_masked_columns //= mask_specgram.size(0) + + assert mask_specgram.size() == specgram.size() + assert num_masked_columns < mask_param + + +@pytest.mark.parametrize('mask_param', [100]) +@pytest.mark.parametrize('mask_value', [0., 30.]) +@pytest.mark.parametrize('axis', [2, 3]) +def test_mask_along_axis_iid(mask_param, mask_value, axis): + torch.random.manual_seed(42) + specgrams = torch.randn(4, 2, 1025, 400) + + mask_specgrams = F.mask_along_axis_iid(specgrams, mask_param, mask_value, axis) + + other_axis = 2 if axis == 3 else 3 + + masked_columns = (mask_specgrams == mask_value).sum(other_axis) + num_masked_columns = (masked_columns == mask_specgrams.size(other_axis)).sum(-1) + + assert mask_specgrams.size() == specgrams.size() + assert (num_masked_columns < mask_param).sum() == num_masked_columns.numel() diff --git a/test/functional_cuda_test.py b/test/torchaudio_unittest/functional/functional_cuda_test.py similarity index 88% rename from test/functional_cuda_test.py rename to test/torchaudio_unittest/functional/functional_cuda_test.py index 9602dcf071..80ac5fa0c2 100644 --- a/test/functional_cuda_test.py +++ b/test/torchaudio_unittest/functional/functional_cuda_test.py @@ -1,6 +1,6 @@ import torch -from . import common_utils +from torchaudio_unittest import common_utils from .functional_impl import Lfilter diff --git a/test/functional_impl.py b/test/torchaudio_unittest/functional/functional_impl.py similarity index 96% rename from test/functional_impl.py rename to test/torchaudio_unittest/functional/functional_impl.py index fb5e7dc7ac..c63001a68e 100644 --- a/test/functional_impl.py +++ b/test/torchaudio_unittest/functional/functional_impl.py @@ -2,7 +2,7 @@ import torch import torchaudio.functional as F -from . import common_utils +from torchaudio_unittest import common_utils class Lfilter(common_utils.TestBaseMixin): diff --git a/test/test_io.py b/test/torchaudio_unittest/io_test.py similarity index 95% rename from test/test_io.py rename to test/torchaudio_unittest/io_test.py index b55885059a..e30f4c3fd3 100644 --- a/test/test_io.py +++ b/test/torchaudio_unittest/io_test.py @@ -6,8 +6,25 @@ import torch import torchaudio +from torchaudio.utils import sox_utils +from torchaudio._internal.module_utils import is_module_available -from .common_utils import BACKENDS, BACKENDS_MP3, get_asset_path +from torchaudio_unittest.common_utils import get_asset_path + +BACKENDS = [] +BACKENDS_MP3 = [] + +if is_module_available('soundfile'): + BACKENDS.append('soundfile') + +if is_module_available('torchaudio._torchaudio'): + BACKENDS.append('sox') + + if ( + 'mp3' in sox_utils.list_read_formats() and + 'mp3' in sox_utils.list_write_formats() + ): + BACKENDS_MP3 = ['sox'] def create_temp_assets_dir(): @@ -30,15 +47,11 @@ class Test_LoadSave(unittest.TestCase): def test_1_save(self): for backend in BACKENDS_MP3: - if backend == 'sox_io': - continue with self.subTest(): torchaudio.set_audio_backend(backend) self._test_1_save(self.test_filepath, False) for backend in BACKENDS: - if backend == 'sox_io': - continue with self.subTest(): torchaudio.set_audio_backend(backend) self._test_1_save(self.test_filepath_wav, True) @@ -85,8 +98,6 @@ def _test_1_save(self, test_filepath, normalization): def test_1_save_sine(self): for backend in BACKENDS: - if backend == 'sox_io': - continue with self.subTest(): torchaudio.set_audio_backend(backend) self._test_1_save_sine() @@ -120,15 +131,11 @@ def _test_1_save_sine(self): def test_2_load(self): for backend in BACKENDS_MP3: - if backend == 'sox_io': - continue with self.subTest(): torchaudio.set_audio_backend(backend) self._test_2_load(self.test_filepath, 278756) for backend in BACKENDS: - if backend == 'sox_io': - continue with self.subTest(): torchaudio.set_audio_backend(backend) self._test_2_load(self.test_filepath_wav, 276858) @@ -224,8 +231,6 @@ def _test_3_load_and_save_is_identity_across_backend(self, backend1, backend2): def test_4_load_partial(self): for backend in BACKENDS_MP3: - if backend == 'sox_io': - continue with self.subTest(): torchaudio.set_audio_backend(backend) self._test_4_load_partial() @@ -268,8 +273,6 @@ def _test_4_load_partial(self): def test_5_get_info(self): for backend in BACKENDS: - if backend == 'sox_io': - continue with self.subTest(): torchaudio.set_audio_backend(backend) self._test_5_get_info() diff --git a/test/kaldi_compatibility_cpu_test.py b/test/torchaudio_unittest/kaldi_compatibility_cpu_test.py similarity index 87% rename from test/kaldi_compatibility_cpu_test.py rename to test/torchaudio_unittest/kaldi_compatibility_cpu_test.py index 3357123296..43be412b47 100644 --- a/test/kaldi_compatibility_cpu_test.py +++ b/test/torchaudio_unittest/kaldi_compatibility_cpu_test.py @@ -1,6 +1,6 @@ import torch -from . import common_utils +from torchaudio_unittest import common_utils from .kaldi_compatibility_impl import Kaldi diff --git a/test/kaldi_compatibility_cuda_test.py b/test/torchaudio_unittest/kaldi_compatibility_cuda_test.py similarity index 88% rename from test/kaldi_compatibility_cuda_test.py rename to test/torchaudio_unittest/kaldi_compatibility_cuda_test.py index e23dbf3f40..28adb7fce5 100644 --- a/test/kaldi_compatibility_cuda_test.py +++ b/test/torchaudio_unittest/kaldi_compatibility_cuda_test.py @@ -1,6 +1,6 @@ import torch -from . import common_utils +from torchaudio_unittest import common_utils from .kaldi_compatibility_impl import Kaldi diff --git a/test/kaldi_compatibility_impl.py b/test/torchaudio_unittest/kaldi_compatibility_impl.py similarity index 98% rename from test/kaldi_compatibility_impl.py rename to test/torchaudio_unittest/kaldi_compatibility_impl.py index 7e51620f63..bae01203f1 100644 --- a/test/kaldi_compatibility_impl.py +++ b/test/torchaudio_unittest/kaldi_compatibility_impl.py @@ -7,7 +7,7 @@ import torchaudio.compliance.kaldi from parameterized import parameterized -from .common_utils import ( +from torchaudio_unittest.common_utils import ( TestBaseMixin, load_params, skipIfNoExec, diff --git a/test/test_kaldi_io.py b/test/torchaudio_unittest/kaldi_io_test.py similarity index 96% rename from test/test_kaldi_io.py rename to test/torchaudio_unittest/kaldi_io_test.py index 80e6b3dc5a..2153b4cc02 100644 --- a/test/test_kaldi_io.py +++ b/test/torchaudio_unittest/kaldi_io_test.py @@ -3,7 +3,7 @@ import torch import torchaudio.kaldi_io as kio -from . import common_utils +from torchaudio_unittest import common_utils class Test_KaldiIO(common_utils.TorchaudioTestCase): diff --git a/test/test_librosa_compatibility.py b/test/torchaudio_unittest/librosa_compatibility_test.py similarity index 99% rename from test/test_librosa_compatibility.py rename to test/torchaudio_unittest/librosa_compatibility_test.py index 01b0f23732..f779f36dae 100644 --- a/test/test_librosa_compatibility.py +++ b/test/torchaudio_unittest/librosa_compatibility_test.py @@ -17,7 +17,7 @@ import pytest -from . import common_utils +from torchaudio_unittest import common_utils @unittest.skipIf(not LIBROSA_AVAILABLE, "Librosa not available") diff --git a/test/test_models.py b/test/torchaudio_unittest/models_test.py similarity index 75% rename from test/test_models.py rename to test/torchaudio_unittest/models_test.py index 10ca5c827b..f9e96edd27 100644 --- a/test/test_models.py +++ b/test/torchaudio_unittest/models_test.py @@ -1,7 +1,7 @@ import torch -from torchaudio.models import Wav2Letter, _MelResNet, _UpsampleNetwork, _WaveRNN +from torchaudio.models import Wav2Letter, MelResNet, UpsampleNetwork, WaveRNN -from . import common_utils +from torchaudio_unittest import common_utils class TestWav2Letter(common_utils.TorchaudioTestCase): @@ -36,7 +36,7 @@ def test_mfcc(self): class TestMelResNet(common_utils.TorchaudioTestCase): def test_waveform(self): - """Validate the output dimensions of a _MelResNet block. + """Validate the output dimensions of a MelResNet block. """ n_batch = 2 @@ -47,7 +47,7 @@ def test_waveform(self): n_hidden = 128 kernel_size = 5 - model = _MelResNet(n_res_block, n_freq, n_hidden, n_output, kernel_size) + model = MelResNet(n_res_block, n_freq, n_hidden, n_output, kernel_size) x = torch.rand(n_batch, n_freq, n_time) out = model(x) @@ -58,7 +58,7 @@ def test_waveform(self): class TestUpsampleNetwork(common_utils.TorchaudioTestCase): def test_waveform(self): - """Validate the output dimensions of a _UpsampleNetwork block. + """Validate the output dimensions of a UpsampleNetwork block. """ upsample_scales = [5, 5, 8] @@ -74,12 +74,12 @@ def test_waveform(self): for upsample_scale in upsample_scales: total_scale *= upsample_scale - model = _UpsampleNetwork(upsample_scales, - n_res_block, - n_freq, - n_hidden, - n_output, - kernel_size) + model = UpsampleNetwork(upsample_scales, + n_res_block, + n_freq, + n_hidden, + n_output, + kernel_size) x = torch.rand(n_batch, n_freq, n_time) out1, out2 = model(x) @@ -91,7 +91,7 @@ def test_waveform(self): class TestWaveRNN(common_utils.TorchaudioTestCase): def test_waveform(self): - """Validate the output dimensions of a _WaveRNN model. + """Validate the output dimensions of a WaveRNN model. """ upsample_scales = [5, 5, 8] @@ -107,8 +107,8 @@ def test_waveform(self): n_hidden = 128 kernel_size = 5 - model = _WaveRNN(upsample_scales, n_classes, hop_length, n_res_block, - n_rnn, n_fc, kernel_size, n_freq, n_hidden, n_output) + model = WaveRNN(upsample_scales, n_classes, hop_length, n_res_block, + n_rnn, n_fc, kernel_size, n_freq, n_hidden, n_output) x = torch.rand(n_batch, 1, hop_length * (n_time - kernel_size + 1)) mels = torch.rand(n_batch, 1, n_freq, n_time) diff --git a/test/test_sox_compatibility.py b/test/torchaudio_unittest/sox_compatibility_test.py similarity index 99% rename from test/test_sox_compatibility.py rename to test/torchaudio_unittest/sox_compatibility_test.py index 260c435b83..ade98b2aeb 100644 --- a/test/test_sox_compatibility.py +++ b/test/torchaudio_unittest/sox_compatibility_test.py @@ -5,7 +5,7 @@ import torchaudio.transforms as T from parameterized import parameterized -from .common_utils import ( +from torchaudio_unittest.common_utils import ( skipIfNoSoxBackend, skipIfNoExec, TempDirMixin, diff --git a/test/sox_io_backend/__init__.py b/test/torchaudio_unittest/sox_effect/__init__.py similarity index 100% rename from test/sox_io_backend/__init__.py rename to test/torchaudio_unittest/sox_effect/__init__.py diff --git a/test/sox_effect/common.py b/test/torchaudio_unittest/sox_effect/common.py similarity index 92% rename from test/sox_effect/common.py rename to test/torchaudio_unittest/sox_effect/common.py index 86edcf6476..00ac4a4565 100644 --- a/test/sox_effect/common.py +++ b/test/torchaudio_unittest/sox_effect/common.py @@ -2,7 +2,7 @@ from parameterized import param -from ..common_utils import get_asset_path +from torchaudio_unittest.common_utils import get_asset_path def name_func(func, _, params): diff --git a/test/sox_effect/test_dataset.py b/test/torchaudio_unittest/sox_effect/dataset_test.py similarity index 98% rename from test/sox_effect/test_dataset.py rename to test/torchaudio_unittest/sox_effect/dataset_test.py index f1b71eb806..e98095d0dd 100644 --- a/test/sox_effect/test_dataset.py +++ b/test/torchaudio_unittest/sox_effect/dataset_test.py @@ -4,7 +4,7 @@ import torch import torchaudio -from ..common_utils import ( +from torchaudio_unittest.common_utils import ( TempDirMixin, PytorchTestCase, skipIfNoExtension, diff --git a/test/sox_effect/smoke_test.py b/test/torchaudio_unittest/sox_effect/smoke_test.py similarity index 97% rename from test/sox_effect/smoke_test.py rename to test/torchaudio_unittest/sox_effect/smoke_test.py index 4997002170..996c62063f 100644 --- a/test/sox_effect/smoke_test.py +++ b/test/torchaudio_unittest/sox_effect/smoke_test.py @@ -1,7 +1,7 @@ from torchaudio import sox_effects from parameterized import parameterized -from ..common_utils import ( +from torchaudio_unittest.common_utils import ( TempDirMixin, TorchaudioTestCase, skipIfNoExtension, diff --git a/test/sox_effect/test_sox_effect.py b/test/torchaudio_unittest/sox_effect/sox_effect_test.py similarity index 99% rename from test/sox_effect/test_sox_effect.py rename to test/torchaudio_unittest/sox_effect/sox_effect_test.py index 38ab0fa772..27740ca576 100644 --- a/test/sox_effect/test_sox_effect.py +++ b/test/torchaudio_unittest/sox_effect/sox_effect_test.py @@ -3,7 +3,7 @@ from torchaudio import sox_effects from parameterized import parameterized -from ..common_utils import ( +from torchaudio_unittest.common_utils import ( TempDirMixin, PytorchTestCase, skipIfNoExtension, diff --git a/test/sox_effect/sox_effects_chain_test.py b/test/torchaudio_unittest/sox_effect/sox_effects_chain_test.py similarity index 100% rename from test/sox_effect/sox_effects_chain_test.py rename to test/torchaudio_unittest/sox_effect/sox_effects_chain_test.py diff --git a/test/sox_effect/test_torchscript.py b/test/torchaudio_unittest/sox_effect/torchscript_test.py similarity index 98% rename from test/sox_effect/test_torchscript.py rename to test/torchaudio_unittest/sox_effect/torchscript_test.py index ded923ab11..8e1a9a3980 100644 --- a/test/sox_effect/test_torchscript.py +++ b/test/torchaudio_unittest/sox_effect/torchscript_test.py @@ -4,7 +4,7 @@ from torchaudio import sox_effects from parameterized import parameterized -from ..common_utils import ( +from torchaudio_unittest.common_utils import ( TempDirMixin, PytorchTestCase, skipIfNoExtension, diff --git a/test/utils/__init__.py b/test/torchaudio_unittest/sox_io_backend/__init__.py similarity index 100% rename from test/utils/__init__.py rename to test/torchaudio_unittest/sox_io_backend/__init__.py diff --git a/test/sox_io_backend/common.py b/test/torchaudio_unittest/sox_io_backend/common.py similarity index 100% rename from test/sox_io_backend/common.py rename to test/torchaudio_unittest/sox_io_backend/common.py diff --git a/test/sox_io_backend/test_info.py b/test/torchaudio_unittest/sox_io_backend/info_test.py similarity index 98% rename from test/sox_io_backend/test_info.py rename to test/torchaudio_unittest/sox_io_backend/info_test.py index da5207a7e5..f07b72e112 100644 --- a/test/sox_io_backend/test_info.py +++ b/test/torchaudio_unittest/sox_io_backend/info_test.py @@ -3,7 +3,7 @@ from torchaudio.backend import sox_io_backend -from ..common_utils import ( +from torchaudio_unittest.common_utils import ( TempDirMixin, PytorchTestCase, skipIfNoExec, diff --git a/test/sox_io_backend/test_load.py b/test/torchaudio_unittest/sox_io_backend/load_test.py similarity index 99% rename from test/sox_io_backend/test_load.py rename to test/torchaudio_unittest/sox_io_backend/load_test.py index 8366a01f83..1ff8b76dc5 100644 --- a/test/sox_io_backend/test_load.py +++ b/test/torchaudio_unittest/sox_io_backend/load_test.py @@ -3,7 +3,7 @@ from torchaudio.backend import sox_io_backend from parameterized import parameterized -from ..common_utils import ( +from torchaudio_unittest.common_utils import ( TempDirMixin, PytorchTestCase, skipIfNoExec, diff --git a/test/sox_io_backend/test_roundtrip.py b/test/torchaudio_unittest/sox_io_backend/roundtrip_test.py similarity index 97% rename from test/sox_io_backend/test_roundtrip.py rename to test/torchaudio_unittest/sox_io_backend/roundtrip_test.py index 2a051bebd5..85aa3019e0 100644 --- a/test/sox_io_backend/test_roundtrip.py +++ b/test/torchaudio_unittest/sox_io_backend/roundtrip_test.py @@ -3,7 +3,7 @@ from torchaudio.backend import sox_io_backend from parameterized import parameterized -from ..common_utils import ( +from torchaudio_unittest.common_utils import ( TempDirMixin, PytorchTestCase, skipIfNoExec, diff --git a/test/sox_io_backend/test_save.py b/test/torchaudio_unittest/sox_io_backend/save_test.py similarity index 99% rename from test/sox_io_backend/test_save.py rename to test/torchaudio_unittest/sox_io_backend/save_test.py index 53588c456b..8e416b7aa9 100644 --- a/test/sox_io_backend/test_save.py +++ b/test/torchaudio_unittest/sox_io_backend/save_test.py @@ -3,7 +3,7 @@ from torchaudio.backend import sox_io_backend from parameterized import parameterized -from ..common_utils import ( +from torchaudio_unittest.common_utils import ( TempDirMixin, PytorchTestCase, skipIfNoExec, diff --git a/test/sox_io_backend/smoke_test.py b/test/torchaudio_unittest/sox_io_backend/smoke_test.py similarity index 98% rename from test/sox_io_backend/smoke_test.py rename to test/torchaudio_unittest/sox_io_backend/smoke_test.py index 1ae9a8d938..be734afa77 100644 --- a/test/sox_io_backend/smoke_test.py +++ b/test/torchaudio_unittest/sox_io_backend/smoke_test.py @@ -6,7 +6,7 @@ from torchaudio._internal.module_utils import is_module_available from parameterized import parameterized -from ..common_utils import ( +from torchaudio_unittest.common_utils import ( TempDirMixin, TorchaudioTestCase, skipIfNoExtension, diff --git a/test/sox_io_backend/test_torchscript.py b/test/torchaudio_unittest/sox_io_backend/torchscript_test.py similarity index 99% rename from test/sox_io_backend/test_torchscript.py rename to test/torchaudio_unittest/sox_io_backend/torchscript_test.py index 9a30aab0d2..dad678cbc0 100644 --- a/test/sox_io_backend/test_torchscript.py +++ b/test/torchaudio_unittest/sox_io_backend/torchscript_test.py @@ -5,7 +5,7 @@ import torchaudio from parameterized import parameterized -from ..common_utils import ( +from torchaudio_unittest.common_utils import ( TempDirMixin, TorchaudioTestCase, skipIfNoExec, diff --git a/test/torchscript_consistency_cpu_test.py b/test/torchaudio_unittest/torchscript_consistency_cpu_test.py similarity index 93% rename from test/torchscript_consistency_cpu_test.py rename to test/torchaudio_unittest/torchscript_consistency_cpu_test.py index 38fe7961fd..fcc9cb8389 100644 --- a/test/torchscript_consistency_cpu_test.py +++ b/test/torchaudio_unittest/torchscript_consistency_cpu_test.py @@ -1,6 +1,6 @@ import torch -from . import common_utils +from torchaudio_unittest import common_utils from .torchscript_consistency_impl import Functional, Transforms diff --git a/test/torchscript_consistency_cuda_test.py b/test/torchaudio_unittest/torchscript_consistency_cuda_test.py similarity index 94% rename from test/torchscript_consistency_cuda_test.py rename to test/torchaudio_unittest/torchscript_consistency_cuda_test.py index b317334ce4..c205e192ed 100644 --- a/test/torchscript_consistency_cuda_test.py +++ b/test/torchaudio_unittest/torchscript_consistency_cuda_test.py @@ -1,6 +1,6 @@ import torch -from . import common_utils +from torchaudio_unittest import common_utils from .torchscript_consistency_impl import Functional, Transforms diff --git a/test/torchscript_consistency_impl.py b/test/torchaudio_unittest/torchscript_consistency_impl.py similarity index 99% rename from test/torchscript_consistency_impl.py rename to test/torchaudio_unittest/torchscript_consistency_impl.py index 76444dc18a..8d14e538fd 100644 --- a/test/torchscript_consistency_impl.py +++ b/test/torchaudio_unittest/torchscript_consistency_impl.py @@ -5,7 +5,7 @@ import torchaudio.functional as F import torchaudio.transforms as T -from . import common_utils +from torchaudio_unittest import common_utils class Functional(common_utils.TestBaseMixin): diff --git a/test/test_transforms.py b/test/torchaudio_unittest/transforms_test.py similarity index 99% rename from test/test_transforms.py rename to test/torchaudio_unittest/transforms_test.py index f6942e18a5..4a91d40bd9 100644 --- a/test/test_transforms.py +++ b/test/torchaudio_unittest/transforms_test.py @@ -6,7 +6,7 @@ import torchaudio.transforms as transforms import torchaudio.functional as F -from . import common_utils +from torchaudio_unittest import common_utils class Tester(common_utils.TorchaudioTestCase): diff --git a/test/torchaudio_unittest/utils/__init__.py b/test/torchaudio_unittest/utils/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/test/utils/test_sox_utils.py b/test/torchaudio_unittest/utils/sox_utils_test.py similarity index 97% rename from test/utils/test_sox_utils.py rename to test/torchaudio_unittest/utils/sox_utils_test.py index 55a46a235e..7c206fdc0b 100644 --- a/test/utils/test_sox_utils.py +++ b/test/torchaudio_unittest/utils/sox_utils_test.py @@ -1,6 +1,6 @@ from torchaudio.utils import sox_utils -from ..common_utils import ( +from torchaudio_unittest.common_utils import ( PytorchTestCase, skipIfNoExtension, ) diff --git a/torchaudio/compliance/kaldi.py b/torchaudio/compliance/kaldi.py index 6211b09f2d..792f439ccd 100644 --- a/torchaudio/compliance/kaldi.py +++ b/torchaudio/compliance/kaldi.py @@ -135,13 +135,15 @@ def _get_waveform_and_window_properties(waveform: Tensor, r"""Gets the waveform and window properties """ channel = max(channel, 0) - assert channel < waveform.size(0), ('Invalid channel %d for size %d' % (channel, waveform.size(0))) + assert channel < waveform.size(0), ('Invalid channel {} for size {}'.format(channel, waveform.size(0))) waveform = waveform[channel, :] # size (n) window_shift = int(sample_frequency * frame_shift * MILLISECONDS_TO_SECONDS) window_size = int(sample_frequency * frame_length * MILLISECONDS_TO_SECONDS) padded_window_size = _next_power_of_2(window_size) if round_to_power_of_two else window_size - assert 2 <= window_size <= len(waveform), ('choose a window size %d that is [2, %d]' % (window_size, len(waveform))) + assert 2 <= window_size <= len( + waveform), ('choose a window size {} that is [2, {}]' + .format(window_size, len(waveform))) assert 0 < window_shift, '`window_shift` must be greater than 0' assert padded_window_size % 2 == 0, 'the padded `window_size` must be divisible by two.' \ ' use `round_to_power_of_two` or change `frame_length`' @@ -430,7 +432,7 @@ def get_mel_banks(num_bins: int, high_freq += nyquist assert (0.0 <= low_freq < nyquist) and (0.0 < high_freq <= nyquist) and (low_freq < high_freq), \ - ('Bad values in options: low-freq %f and high-freq %f vs. nyquist %f' % (low_freq, high_freq, nyquist)) + ('Bad values in options: low-freq {} and high-freq {} vs. nyquist {}'.format(low_freq, high_freq, nyquist)) # fft-bin width [think of it as Nyquist-freq / half-window-length] fft_bin_width = sample_freq / window_length_padded @@ -446,8 +448,8 @@ def get_mel_banks(num_bins: int, assert vtln_warp_factor == 1.0 or ((low_freq < vtln_low < high_freq) and (0.0 < vtln_high < high_freq) and (vtln_low < vtln_high)), \ - ('Bad values in options: vtln-low %f and vtln-high %f, versus low-freq %f and high-freq %f' % - (vtln_low, vtln_high, low_freq, high_freq)) + ('Bad values in options: vtln-low {} and vtln-high {}, versus ' + 'low-freq {} and high-freq {}'.format(vtln_low, vtln_high, low_freq, high_freq)) bin = torch.arange(num_bins).unsqueeze(1) left_mel = mel_low_freq + bin * mel_freq_delta # size(num_bins, 1) diff --git a/torchaudio/datasets/ljspeech.py b/torchaudio/datasets/ljspeech.py index 0b4ee86acc..5e3b6ee7d2 100644 --- a/torchaudio/datasets/ljspeech.py +++ b/torchaudio/datasets/ljspeech.py @@ -63,7 +63,7 @@ def __init__(self, download_url(url, root, hash_value=checksum) extract_archive(archive) - with open(self._metadata_path, "r") as metadata: + with open(self._metadata_path, "r", newline='') as metadata: walker = unicode_csv_reader(metadata, delimiter="|", quoting=csv.QUOTE_NONE) self._walker = list(walker) diff --git a/torchaudio/functional.py b/torchaudio/functional.py index 53795fef1a..4b79c01960 100644 --- a/torchaudio/functional.py +++ b/torchaudio/functional.py @@ -8,7 +8,6 @@ from torch import Tensor __all__ = [ - "istft", "spectrogram", "griffinlim", "amplitude_to_DB", @@ -45,79 +44,6 @@ ] -def istft( - stft_matrix: Tensor, - n_fft: int, - hop_length: Optional[int] = None, - win_length: Optional[int] = None, - window: Optional[Tensor] = None, - center: bool = True, - pad_mode: Optional[str] = None, - normalized: bool = False, - onesided: bool = True, - length: Optional[int] = None, -) -> Tensor: - r"""Inverse short time Fourier Transform. This is expected to be the inverse of torch.stft. - It has the same parameters (+ additional optional parameter of ``length``) and it should return the - least squares estimation of the original signal. The algorithm will check using the NOLA condition ( - nonzero overlap). - - Important consideration in the parameters ``window`` and ``center`` so that the envelop - created by the summation of all the windows is never zero at certain point in time. Specifically, - :math:`\sum_{t=-\infty}^{\infty} w^2[n-t\times hop\_length] \cancel{=} 0`. - - Since stft discards elements at the end of the signal if they do not fit in a frame, the - istft may return a shorter signal than the original signal (can occur if ``center`` is False - since the signal isn't padded). - - If ``center`` is True, then there will be padding e.g. 'constant', 'reflect', etc. Left padding - can be trimmed off exactly because they can be calculated but right padding cannot be calculated - without additional information. - - Example: Suppose the last window is: - [17, 18, 0, 0, 0] vs [18, 0, 0, 0, 0] - - The n_frame, hop_length, win_length are all the same which prevents the calculation of right padding. - These additional values could be zeros or a reflection of the signal so providing ``length`` - could be useful. If ``length`` is ``None`` then padding will be aggressively removed - (some loss of signal). - - [1] D. W. Griffin and J. S. Lim, "Signal estimation from modified short-time Fourier transform," - IEEE Trans. ASSP, vol.32, no.2, pp.236-243, Apr. 1984. - - Args: - stft_matrix (Tensor): Output of stft where each row of a channel is a frequency and each - column is a window. It has a size of either (..., fft_size, n_frame, 2) - n_fft (int): Size of Fourier transform - hop_length (int or None, optional): The distance between neighboring sliding window frames. - (Default: ``win_length // 4``) - win_length (int or None, optional): The size of window frame and STFT filter. (Default: ``n_fft``) - window (Tensor or None, optional): The optional window function. - (Default: ``torch.ones(win_length)``) - center (bool, optional): Whether ``input`` was padded on both sides so - that the :math:`t`-th frame is centered at time :math:`t \times \text{hop\_length}`. - (Default: ``True``) - pad_mode: This argument was ignored and to be removed. - normalized (bool, optional): Whether the STFT was normalized. (Default: ``False``) - onesided (bool, optional): Whether the STFT is onesided. (Default: ``True``) - length (int or None, optional): The amount to trim the signal by (i.e. the - original signal length). (Default: whole signal) - - Returns: - Tensor: Least squares estimation of the original signal of size (..., signal_length) - """ - warnings.warn( - 'istft has been moved to PyTorch and will be removed from torchaudio, ' - 'please use torch.istft instead.') - if pad_mode is not None: - warnings.warn( - 'The parameter `pad_mode` was ignored in isftft, and is thus being deprecated. ' - 'Please set `pad_mode` to None to suppress this warning.') - return torch.istft( - input=stft_matrix, n_fft=n_fft, hop_length=hop_length, win_length=win_length, window=window, - center=center, normalized=normalized, onesided=onesided, length=length) - - def spectrogram( waveform: Tensor, pad: int, @@ -250,12 +176,12 @@ def griffinlim( tprev = rebuilt # Invert with our current estimate of the phases - inverse = istft(specgram * angles, - n_fft=n_fft, - hop_length=hop_length, - win_length=win_length, - window=window, - length=length).float() + inverse = torch.istft(specgram * angles, + n_fft=n_fft, + hop_length=hop_length, + win_length=win_length, + window=window, + length=length).float() # Rebuild the spectrogram rebuilt = torch.stft(inverse, n_fft, hop_length, win_length, window, @@ -268,12 +194,12 @@ def griffinlim( angles = angles.div(complex_norm(angles).add(1e-16).unsqueeze(-1).expand_as(angles)) # Return the final phase estimates - waveform = istft(specgram * angles, - n_fft=n_fft, - hop_length=hop_length, - win_length=win_length, - window=window, - length=length) + waveform = torch.istft(specgram * angles, + n_fft=n_fft, + hop_length=hop_length, + win_length=win_length, + window=window, + length=length) # unpack batch waveform = waveform.reshape(shape[:-2] + waveform.shape[-1:]) diff --git a/torchaudio/models/__init__.py b/torchaudio/models/__init__.py index 20c1bdf265..8e05b8b509 100644 --- a/torchaudio/models/__init__.py +++ b/torchaudio/models/__init__.py @@ -1,2 +1,2 @@ from .wav2letter import * -from ._wavernn import * +from .wavernn import * diff --git a/torchaudio/models/_wavernn.py b/torchaudio/models/wavernn.py similarity index 83% rename from torchaudio/models/_wavernn.py rename to torchaudio/models/wavernn.py index 37a06edfc3..8fbd111a72 100644 --- a/torchaudio/models/_wavernn.py +++ b/torchaudio/models/wavernn.py @@ -4,10 +4,10 @@ from torch import Tensor from torch import nn -__all__ = ["_ResBlock", "_MelResNet", "_Stretch2d", "_UpsampleNetwork", "_WaveRNN"] +__all__ = ["ResBlock", "MelResNet", "Stretch2d", "UpsampleNetwork", "WaveRNN"] -class _ResBlock(nn.Module): +class ResBlock(nn.Module): r"""ResNet block based on "Deep Residual Learning for Image Recognition" The paper link is https://arxiv.org/pdf/1512.03385.pdf. @@ -16,7 +16,7 @@ class _ResBlock(nn.Module): n_freq: the number of bins in a spectrogram. (Default: ``128``) Examples - >>> resblock = _ResBlock() + >>> resblock = ResBlock() >>> input = torch.rand(10, 128, 512) # a random spectrogram >>> output = resblock(input) # shape: (10, 128, 512) """ @@ -33,9 +33,9 @@ def __init__(self, n_freq: int = 128) -> None: ) def forward(self, specgram: Tensor) -> Tensor: - r"""Pass the input through the _ResBlock layer. + r"""Pass the input through the ResBlock layer. Args: - specgram (Tensor): the input sequence to the _ResBlock layer (n_batch, n_freq, n_time). + specgram (Tensor): the input sequence to the ResBlock layer (n_batch, n_freq, n_time). Return: Tensor shape: (n_batch, n_freq, n_time) @@ -44,7 +44,7 @@ def forward(self, specgram: Tensor) -> Tensor: return self.resblock_model(specgram) + specgram -class _MelResNet(nn.Module): +class MelResNet(nn.Module): r"""MelResNet layer uses a stack of ResBlocks on spectrogram. Args: @@ -55,7 +55,7 @@ class _MelResNet(nn.Module): kernel_size: the number of kernel size in the first Conv1d layer. (Default: ``5``) Examples - >>> melresnet = _MelResNet() + >>> melresnet = MelResNet() >>> input = torch.rand(10, 128, 512) # a random spectrogram >>> output = melresnet(input) # shape: (10, 128, 508) """ @@ -68,7 +68,7 @@ def __init__(self, kernel_size: int = 5) -> None: super().__init__() - ResBlocks = [_ResBlock(n_hidden) for _ in range(n_res_block)] + ResBlocks = [ResBlock(n_hidden) for _ in range(n_res_block)] self.melresnet_model = nn.Sequential( nn.Conv1d(in_channels=n_freq, out_channels=n_hidden, kernel_size=kernel_size, bias=False), @@ -79,9 +79,9 @@ def __init__(self, ) def forward(self, specgram: Tensor) -> Tensor: - r"""Pass the input through the _MelResNet layer. + r"""Pass the input through the MelResNet layer. Args: - specgram (Tensor): the input sequence to the _MelResNet layer (n_batch, n_freq, n_time). + specgram (Tensor): the input sequence to the MelResNet layer (n_batch, n_freq, n_time). Return: Tensor shape: (n_batch, n_output, n_time - kernel_size + 1) @@ -90,7 +90,7 @@ def forward(self, specgram: Tensor) -> Tensor: return self.melresnet_model(specgram) -class _Stretch2d(nn.Module): +class Stretch2d(nn.Module): r"""Upscale the frequency and time dimensions of a spectrogram. Args: @@ -98,7 +98,7 @@ class _Stretch2d(nn.Module): freq_scale: the scale factor in frequency dimension Examples - >>> stretch2d = _Stretch2d(time_scale=10, freq_scale=5) + >>> stretch2d = Stretch2d(time_scale=10, freq_scale=5) >>> input = torch.rand(10, 100, 512) # a random spectrogram >>> output = stretch2d(input) # shape: (10, 500, 5120) @@ -113,10 +113,10 @@ def __init__(self, self.time_scale = time_scale def forward(self, specgram: Tensor) -> Tensor: - r"""Pass the input through the _Stretch2d layer. + r"""Pass the input through the Stretch2d layer. Args: - specgram (Tensor): the input sequence to the _Stretch2d layer (..., n_freq, n_time). + specgram (Tensor): the input sequence to the Stretch2d layer (..., n_freq, n_time). Return: Tensor shape: (..., n_freq * freq_scale, n_time * time_scale) @@ -125,7 +125,7 @@ def forward(self, specgram: Tensor) -> Tensor: return specgram.repeat_interleave(self.freq_scale, -2).repeat_interleave(self.time_scale, -1) -class _UpsampleNetwork(nn.Module): +class UpsampleNetwork(nn.Module): r"""Upscale the dimensions of a spectrogram. Args: @@ -137,7 +137,7 @@ class _UpsampleNetwork(nn.Module): kernel_size: the number of kernel size in the first Conv1d layer. (Default: ``5``) Examples - >>> upsamplenetwork = _UpsampleNetwork(upsample_scales=[4, 4, 16]) + >>> upsamplenetwork = UpsampleNetwork(upsample_scales=[4, 4, 16]) >>> input = torch.rand(10, 128, 10) # a random spectrogram >>> output = upsamplenetwork(input) # shape: (10, 1536, 128), (10, 1536, 128) """ @@ -156,12 +156,12 @@ def __init__(self, total_scale *= upsample_scale self.indent = (kernel_size - 1) // 2 * total_scale - self.resnet = _MelResNet(n_res_block, n_freq, n_hidden, n_output, kernel_size) - self.resnet_stretch = _Stretch2d(total_scale, 1) + self.resnet = MelResNet(n_res_block, n_freq, n_hidden, n_output, kernel_size) + self.resnet_stretch = Stretch2d(total_scale, 1) up_layers = [] for scale in upsample_scales: - stretch = _Stretch2d(scale, 1) + stretch = Stretch2d(scale, 1) conv = nn.Conv2d(in_channels=1, out_channels=1, kernel_size=(1, scale * 2 + 1), @@ -173,10 +173,10 @@ def __init__(self, self.upsample_layers = nn.Sequential(*up_layers) def forward(self, specgram: Tensor) -> Tuple[Tensor, Tensor]: - r"""Pass the input through the _UpsampleNetwork layer. + r"""Pass the input through the UpsampleNetwork layer. Args: - specgram (Tensor): the input sequence to the _UpsampleNetwork layer (n_batch, n_freq, n_time) + specgram (Tensor): the input sequence to the UpsampleNetwork layer (n_batch, n_freq, n_time) Return: Tensor shape: (n_batch, n_freq, (n_time - kernel_size + 1) * total_scale), @@ -195,7 +195,7 @@ def forward(self, specgram: Tensor) -> Tuple[Tensor, Tensor]: return upsampling_output, resnet_output -class _WaveRNN(nn.Module): +class WaveRNN(nn.Module): r"""WaveRNN model based on the implementation from `fatchord `_. The original implementation was introduced in @@ -216,7 +216,7 @@ class _WaveRNN(nn.Module): n_output: the number of output dimensions of melresnet. (Default: ``128``) Example - >>> wavernn = _waveRNN(upsample_scales=[5,5,8], n_classes=512, hop_length=200) + >>> wavernn = WaveRNN(upsample_scales=[5,5,8], n_classes=512, hop_length=200) >>> waveform, sample_rate = torchaudio.load(file) >>> # waveform shape: (n_batch, n_channel, (n_time - kernel_size + 1) * hop_length) >>> specgram = MelSpectrogram(sample_rate)(waveform) # shape: (n_batch, n_channel, n_freq, n_time) @@ -249,12 +249,12 @@ def __init__(self, if total_scale != self.hop_length: raise ValueError(f"Expected: total_scale == hop_length, but found {total_scale} != {hop_length}") - self.upsample = _UpsampleNetwork(upsample_scales, - n_res_block, - n_freq, - n_hidden, - n_output, - kernel_size) + self.upsample = UpsampleNetwork(upsample_scales, + n_res_block, + n_freq, + n_hidden, + n_output, + kernel_size) self.fc = nn.Linear(n_freq + self.n_aux + 1, n_rnn) self.rnn1 = nn.GRU(n_rnn, n_rnn, batch_first=True) @@ -268,11 +268,11 @@ def __init__(self, self.fc3 = nn.Linear(n_fc, self.n_classes) def forward(self, waveform: Tensor, specgram: Tensor) -> Tensor: - r"""Pass the input through the _WaveRNN model. + r"""Pass the input through the WaveRNN model. Args: - waveform: the input waveform to the _WaveRNN layer (n_batch, 1, (n_time - kernel_size + 1) * hop_length) - specgram: the input spectrogram to the _WaveRNN layer (n_batch, 1, n_freq, n_time) + waveform: the input waveform to the WaveRNN layer (n_batch, 1, (n_time - kernel_size + 1) * hop_length) + specgram: the input spectrogram to the WaveRNN layer (n_batch, 1, n_freq, n_time) Return: Tensor shape: (n_batch, 1, (n_time - kernel_size + 1) * hop_length, n_classes) diff --git a/torchaudio/transforms.py b/torchaudio/transforms.py index ea0d6db492..ef0a58e637 100644 --- a/torchaudio/transforms.py +++ b/torchaudio/transforms.py @@ -141,8 +141,8 @@ def __init__(self, rand_init: bool = True) -> None: super(GriffinLim, self).__init__() - assert momentum < 1, 'momentum=%s > 1 can be unstable' % momentum - assert momentum > 0, 'momentum=%s < 0' % momentum + assert momentum < 1, 'momentum={} > 1 can be unstable'.format(momentum) + assert momentum > 0, 'momentum={} < 0'.format(momentum) self.n_fft = n_fft self.n_iter = n_iter @@ -237,7 +237,7 @@ def __init__(self, self.f_max = f_max if f_max is not None else float(sample_rate // 2) self.f_min = f_min - assert f_min <= self.f_max, 'Require f_min: %f < f_max: %f' % (f_min, self.f_max) + assert f_min <= self.f_max, 'Require f_min: {} < f_max: {}'.format(f_min, self.f_max) fb = torch.empty(0) if n_stft is None else F.create_fb_matrix( n_stft, self.f_min, self.f_max, self.n_mels, self.sample_rate) @@ -313,7 +313,7 @@ def __init__(self, self.tolerance_change = tolerance_change self.sgdargs = sgdargs or {'lr': 0.1, 'momentum': 0.9} - assert f_min <= self.f_max, 'Require f_min: %f < f_max: %f' % (f_min, self.f_max) + assert f_min <= self.f_max, 'Require f_min: {} < f_max: {}'.format(f_min, self.f_max) fb = F.create_fb_matrix(n_stft, self.f_min, self.f_max, self.n_mels, self.sample_rate) self.register_buffer('fb', fb) @@ -607,7 +607,7 @@ def forward(self, waveform: Tensor) -> Tensor: return waveform - raise ValueError('Invalid resampling method: ' + str(self.resampling_method)) + raise ValueError('Invalid resampling method: {}'.format(self.resampling_method)) class ComplexNorm(torch.nn.Module): From 3019ce03a2855abdfc397721f4299ba79fbe7b2e Mon Sep 17 00:00:00 2001 From: Andres Suarez Date: Wed, 5 Aug 2020 18:26:48 -0700 Subject: [PATCH 16/19] Remove .python3 markers Reviewed By: ashwinp-fb Differential Revision: D22955630 fbshipit-source-id: f00ef17a905e4c7cd9196c8924db39f9cdfe8cfa --- .python3 | 0 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 .python3 diff --git a/.python3 b/.python3 deleted file mode 100644 index e69de29bb2..0000000000 From aa8c98d3aca64739266a8fb714d299b5f7ff3507 Mon Sep 17 00:00:00 2001 From: Moto Hira Date: Tue, 22 Sep 2020 10:59:56 -0700 Subject: [PATCH 17/19] Import torchaudio 20200821 Reviewed By: cpuhrsch Differential Revision: D23273584 fbshipit-source-id: 2fe7effa11b7f7cdf0cee1da6b1cac5556e9f55b --- .circleci/config.yml | 42 +++++- .circleci/config.yml.in | 42 +++++- .circleci/torchscript_bc_test/common.sh | 73 +++++++++ .circleci/torchscript_bc_test/environment.yml | 17 +++ .../torchscript_bc_test/generate_objects.sh | 26 ++++ .../torchscript_bc_test/setup_master_envs.sh | 17 +++ .../torchscript_bc_test/setup_release_envs.sh | 18 +++ .../torchscript_bc_test/validate_objects.sh | 30 ++++ .../unittest/linux/scripts/environment.yml | 5 +- .../unittest/windows/scripts/environment.yml | 3 +- docs/source/datasets.rst | 7 + examples/pipeline_wavernn/main.py | 5 +- examples/pipeline_wavernn/processing.py | 13 +- test/README.md | 131 +--------------- test/torchaudio_unittest/README.md | 131 ++++++++++++++++ .../torchaudio_unittest/datasets/vctk_test.py | 91 ++++++++++++ .../sox_io_backend/info_test.py | 14 ++ .../sox_io_backend/load_test.py | 30 ++++ .../sox_io_backend/save_test.py | 40 +++++ .../torchscript_consistency_impl.py | 7 + test/torchscript_bc_test/README.md | 23 +++ test/torchscript_bc_test/main.py | 71 +++++++++ test/torchscript_bc_test/ver_060.py | 71 +++++++++ torchaudio/backend/sox_io_backend.py | 4 +- torchaudio/csrc/register.cpp | 140 ++++++++---------- torchaudio/csrc/sox_utils.cpp | 6 + torchaudio/datasets/__init__.py | 3 +- torchaudio/datasets/vctk.py | 139 +++++++++++++++-- torchaudio/functional.py | 3 +- 29 files changed, 970 insertions(+), 232 deletions(-) create mode 100644 .circleci/torchscript_bc_test/common.sh create mode 100644 .circleci/torchscript_bc_test/environment.yml create mode 100755 .circleci/torchscript_bc_test/generate_objects.sh create mode 100755 .circleci/torchscript_bc_test/setup_master_envs.sh create mode 100755 .circleci/torchscript_bc_test/setup_release_envs.sh create mode 100755 .circleci/torchscript_bc_test/validate_objects.sh create mode 100644 test/torchaudio_unittest/README.md create mode 100644 test/torchaudio_unittest/datasets/vctk_test.py create mode 100644 test/torchscript_bc_test/README.md create mode 100755 test/torchscript_bc_test/main.py create mode 100644 test/torchscript_bc_test/ver_060.py diff --git a/.circleci/config.yml b/.circleci/config.yml index 771dccc515..70b7c54ef5 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -277,12 +277,13 @@ jobs: steps: - attach_workspace: at: ~/workspace + - designate_upload_channel - run: name: install binaries command: | set -x source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION} - conda install -v -y -c pytorch-test -c pytorch-nightly pytorch + conda install -v -y -c pytorch-${UPLOAD_CHANNEL} pytorch conda install -v -y $(ls ~/workspace/torchaudio*.tar.bz2) - run: name: smoke test @@ -604,6 +605,42 @@ jobs: name: Run style check command: .circleci/unittest/linux/scripts/run_style_checks.sh + torchscript_bc_test: + docker: + - image: "pytorch/torchaudio_unittest_base:manylinux" + resource_class: large + steps: + - checkout + - generate_cache_key + - restore_cache: + + keys: + - torchscript-bc-test-release-envs-v1-{{ arch }}-{{ checksum ".cachekey" }} + + - run: + name: Generate Objects + command: | + .circleci/torchscript_bc_test/setup_release_envs.sh + .circleci/torchscript_bc_test/generate_objects.sh + - save_cache: + + key: torchscript-bc-test-release-envs-v1-{{ arch }}-{{ checksum ".cachekey" }} + + paths: + - conda + - envs + - store_artifacts: + path: test/torchscript_bc_test/assets + - persist_to_workspace: + root: . + paths: + - test/torchscript_bc_test/assets + - run: + name: Run BC check test + command: | + .circleci/torchscript_bc_test/setup_master_envs.sh + .circleci/torchscript_bc_test/validate_objects.sh + workflows: build: jobs: @@ -690,6 +727,9 @@ workflows: python_version: '3.8' unittest: jobs: + - torchscript_bc_test: + requires: + - download_third_parties_nix - download_third_parties_nix: name: download_third_parties_nix - unittest_linux_cpu: diff --git a/.circleci/config.yml.in b/.circleci/config.yml.in index 6d6d77d50f..94220d02c0 100644 --- a/.circleci/config.yml.in +++ b/.circleci/config.yml.in @@ -277,12 +277,13 @@ jobs: steps: - attach_workspace: at: ~/workspace + - designate_upload_channel - run: name: install binaries command: | set -x source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION} - conda install -v -y -c pytorch-test -c pytorch-nightly pytorch + conda install -v -y -c pytorch-${UPLOAD_CHANNEL} pytorch conda install -v -y $(ls ~/workspace/torchaudio*.tar.bz2) - run: name: smoke test @@ -604,6 +605,42 @@ jobs: name: Run style check command: .circleci/unittest/linux/scripts/run_style_checks.sh + torchscript_bc_test: + docker: + - image: "pytorch/torchaudio_unittest_base:manylinux" + resource_class: large + steps: + - checkout + - generate_cache_key + - restore_cache: + {% raw %} + keys: + - torchscript-bc-test-release-envs-v1-{{ arch }}-{{ checksum ".cachekey" }} + {% endraw %} + - run: + name: Generate Objects + command: | + .circleci/torchscript_bc_test/setup_release_envs.sh + .circleci/torchscript_bc_test/generate_objects.sh + - save_cache: + {% raw %} + key: torchscript-bc-test-release-envs-v1-{{ arch }}-{{ checksum ".cachekey" }} + {% endraw %} + paths: + - conda + - envs + - store_artifacts: + path: test/torchscript_bc_test/assets + - persist_to_workspace: + root: . + paths: + - test/torchscript_bc_test/assets + - run: + name: Run BC check test + command: | + .circleci/torchscript_bc_test/setup_master_envs.sh + .circleci/torchscript_bc_test/validate_objects.sh + workflows: build: jobs: @@ -611,6 +648,9 @@ workflows: {{ build_workflows() }} unittest: jobs: + - torchscript_bc_test: + requires: + - download_third_parties_nix {{ unittest_workflows() }} nightly: jobs: diff --git a/.circleci/torchscript_bc_test/common.sh b/.circleci/torchscript_bc_test/common.sh new file mode 100644 index 0000000000..51d258a2d9 --- /dev/null +++ b/.circleci/torchscript_bc_test/common.sh @@ -0,0 +1,73 @@ +#!/usr/bin/env bash + +declare -a TORCHAUDIO_VERSIONS=("0.6.0") +declare -a PYTHON_VERSIONS=("3.6" "3.7" "3.8") + +export TORCHAUDIO_VERSIONS +export PYTHON_VERSIONS + +export KALDI_ROOT="${KALDI_ROOT:-$HOME}" # Just to disable warning emitted from kaldi_io + +_this_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" +_root_dir="$(git rev-parse --show-toplevel)" +_conda_dir="${_root_dir}/conda" +case "$(uname -s)" in + Darwin*) _os="MacOSX";; + *) _os="Linux" +esac + +install_conda() { + if [ ! -d "${_conda_dir}" ]; then + printf "* Installing conda\n" + wget -nv -O miniconda.sh "http://repo.continuum.io/miniconda/Miniconda3-latest-${_os}-x86_64.sh" + bash ./miniconda.sh -b -f -p "${_conda_dir}" + rm miniconda.sh + fi +} + +init_conda() { + eval "$("${_conda_dir}/bin/conda" shell.bash hook)" +} + +get_name() { + echo "${1}-py${2}" +} + +get_env_dir() { + echo "${_root_dir}/envs/$(get_name "$1" "$2")" +} + +create_env() { + env_dir="$(get_env_dir "$1" "$2")" + if [ ! -d "${env_dir}" ]; then + printf "* Creating environment torchaudio: %s, Python: %s\n" "$1" "$2" + conda create -q --prefix "${env_dir}" -y python="$2" + fi +} + +activate_env() { + printf "* Activating environment torchaudio: %s, Python: %s\n" "$1" "$2" + conda activate "$(get_env_dir "$1" "$2")" +} + +install_release() { + printf "* Installing torchaudio: %s\n" "$1" + conda install -y -q torchaudio="$1" packaging -c pytorch + # packaging is required in test to validate the torchaudio version for dump +} + +install_build_dependencies() { + printf "* Installing torchaudio dependencies except PyTorch - (Python: %s)\n" "$1" + conda env update -q --file "${_this_dir}/environment.yml" --prune + if [ "${_os}" == Linux ]; then + pip install clang-format + fi +} + +build_master() { + printf "* Installing PyTorch (py%s)\n" "$1" + conda install -y -q pytorch "cpuonly" -c pytorch-nightly + printf "* Installing torchaudio\n" + cd "${_root_dir}" || exit 1 + BUILD_SOX=1 python setup.py clean install +} diff --git a/.circleci/torchscript_bc_test/environment.yml b/.circleci/torchscript_bc_test/environment.yml new file mode 100644 index 0000000000..108d97d193 --- /dev/null +++ b/.circleci/torchscript_bc_test/environment.yml @@ -0,0 +1,17 @@ +channels: + - conda-forge + - defaults +dependencies: + - flake8 + - numpy + - pytest + - pytest-cov + - codecov + - librosa + - llvmlite==0.31 # See https://github.com/pytorch/audio/pull/766 + - pip + - pip: + - kaldi-io + - scipy + - parameterized + - numba==0.48 # See https://github.com/librosa/librosa/issues/1160 diff --git a/.circleci/torchscript_bc_test/generate_objects.sh b/.circleci/torchscript_bc_test/generate_objects.sh new file mode 100755 index 0000000000..352f137ad2 --- /dev/null +++ b/.circleci/torchscript_bc_test/generate_objects.sh @@ -0,0 +1,26 @@ +#!/usr/bin/env bash + +set -e + +this_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" +pushd "${this_dir}" +. "common.sh" +popd + +init_conda + +# Move to test directory so that the checked out torchaudio source +# will not shadow the conda-installed version of torchaudio +cd test + +for torchaudio in "${TORCHAUDIO_VERSIONS[@]}" ; do + for python in "${PYTHON_VERSIONS[@]}" ; do + activate_env "${torchaudio}" "${python}" + python -m torch.utils.collect_env + printf "***********************************************************\n" + printf "* Generating\n" + printf " Objects: Python: %s, torchaudio: %s\n" "${python}" "${torchaudio}" + printf "***********************************************************\n" + ./torchscript_bc_test/main.py --mode generate --version "${torchaudio}" + done +done diff --git a/.circleci/torchscript_bc_test/setup_master_envs.sh b/.circleci/torchscript_bc_test/setup_master_envs.sh new file mode 100755 index 0000000000..2321ee0529 --- /dev/null +++ b/.circleci/torchscript_bc_test/setup_master_envs.sh @@ -0,0 +1,17 @@ +#!/usr/bin/env bash + +set -e + +cd "$( dirname "${BASH_SOURCE[0]}" )" +. "common.sh" + +install_conda +init_conda + +# Install torchaudio environments +for python in "${PYTHON_VERSIONS[@]}" ; do + create_env master "${python}" + activate_env master "${python}" + install_build_dependencies "${python}" + build_master "${python}" +done diff --git a/.circleci/torchscript_bc_test/setup_release_envs.sh b/.circleci/torchscript_bc_test/setup_release_envs.sh new file mode 100755 index 0000000000..3816c091d4 --- /dev/null +++ b/.circleci/torchscript_bc_test/setup_release_envs.sh @@ -0,0 +1,18 @@ +#!/usr/bin/env bash + +set -e + +cd "$( dirname "${BASH_SOURCE[0]}" )" +. "common.sh" + +install_conda +init_conda + +# Install torchaudio environments +for torchaudio in "${TORCHAUDIO_VERSIONS[@]}" ; do + for python in "${PYTHON_VERSIONS[@]}" ; do + create_env "${torchaudio}" "${python}" + activate_env "${torchaudio}" "${python}" + install_release "${torchaudio}" + done +done diff --git a/.circleci/torchscript_bc_test/validate_objects.sh b/.circleci/torchscript_bc_test/validate_objects.sh new file mode 100755 index 0000000000..852ed0acc8 --- /dev/null +++ b/.circleci/torchscript_bc_test/validate_objects.sh @@ -0,0 +1,30 @@ +#!/usr/bin/env bash + +set -e + +this_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" +pushd "${this_dir}" +. "common.sh" +popd + +init_conda + +# Move to test directory so that the checked out torchaudio source +# will not shadow the conda-installed version of torchaudio +cd test + +# Validate torchscript objects for each +for runtime_python in "${PYTHON_VERSIONS[@]}" ; do + activate_env master "${runtime_python}" + python -m torch.utils.collect_env + for object_torchaudio in "${TORCHAUDIO_VERSIONS[@]}" ; do + for object_python in "${PYTHON_VERSIONS[@]}" ; do + printf "***********************************************************\n" + printf "* Validating\n" + printf " Runtime: Python: %s, torchaudio: master (%s)\n" "${runtime_python}" "$(python -c 'import torchaudio;print(torchaudio.__version__)')" + printf " Objects: Python: %s, torchaudio: %s\n" "${object_python}" "${object_torchaudio}" + printf "***********************************************************\n" + ./torchscript_bc_test/main.py --mode validate --version "${object_torchaudio}" + done + done +done diff --git a/.circleci/unittest/linux/scripts/environment.yml b/.circleci/unittest/linux/scripts/environment.yml index 121478165e..f68fb130a2 100644 --- a/.circleci/unittest/linux/scripts/environment.yml +++ b/.circleci/unittest/linux/scripts/environment.yml @@ -3,16 +3,15 @@ channels: - defaults dependencies: - flake8 - - numpy + - numpy >= 1.11 - pytest - pytest-cov - pytest-xdist - codecov - - librosa + - librosa>=0.8.0 - llvmlite==0.31 # See https://github.com/pytorch/audio/pull/766 - pip - pip: - kaldi-io - scipy - parameterized - - numba==0.48 # See https://github.com/librosa/librosa/issues/1160 diff --git a/.circleci/unittest/windows/scripts/environment.yml b/.circleci/unittest/windows/scripts/environment.yml index fb57f0facc..3a41020645 100644 --- a/.circleci/unittest/windows/scripts/environment.yml +++ b/.circleci/unittest/windows/scripts/environment.yml @@ -12,7 +12,6 @@ dependencies: - scipy == 1.4.1 - kaldi-io - PySoundFile - - librosa + - librosa >= 0.8.0 - future - parameterized - - numba==0.48 diff --git a/docs/source/datasets.rst b/docs/source/datasets.rst index 96ba045c5e..6cdf44e223 100644 --- a/docs/source/datasets.rst +++ b/docs/source/datasets.rst @@ -88,6 +88,13 @@ VCTK :members: __getitem__ :special-members: + +VCTK_092 +~~~~~~~~ + +.. autoclass:: VCTK_092 + + YESNO ~~~~~ diff --git a/examples/pipeline_wavernn/main.py b/examples/pipeline_wavernn/main.py index 9620dd5d45..28ecb76fb9 100644 --- a/examples/pipeline_wavernn/main.py +++ b/examples/pipeline_wavernn/main.py @@ -163,6 +163,9 @@ def parse_args(): parser.add_argument( "--file-path", default="", type=str, help="the path of audio files", ) + parser.add_argument( + "--normalization", default=True, action="store_true", help="if True, spectrogram is normalized", + ) args = parser.parse_args() return args @@ -273,7 +276,7 @@ def main(args): n_mels=args.n_freq, fmin=args.f_min, ), - NormalizeDB(min_level_db=args.min_level_db), + NormalizeDB(min_level_db=args.min_level_db, normalization=args.normalization), ) train_dataset, val_dataset = split_process_dataset(args, transforms) diff --git a/examples/pipeline_wavernn/processing.py b/examples/pipeline_wavernn/processing.py index b22d60dae4..dca45e97f4 100644 --- a/examples/pipeline_wavernn/processing.py +++ b/examples/pipeline_wavernn/processing.py @@ -31,15 +31,18 @@ class NormalizeDB(nn.Module): r"""Normalize the spectrogram with a minimum db value """ - def __init__(self, min_level_db): + def __init__(self, min_level_db, normalization): super().__init__() self.min_level_db = min_level_db + self.normalization = normalization def forward(self, specgram): - specgram = 20 * torch.log10(torch.clamp(specgram, min=1e-5)) - return torch.clamp( - (self.min_level_db - specgram) / self.min_level_db, min=0, max=1 - ) + specgram = torch.log10(torch.clamp(specgram, min=1e-5)) + if self.normalization: + return torch.clamp( + (self.min_level_db - 20 * specgram) / self.min_level_db, min=0, max=1 + ) + return specgram def normalized_waveform_to_bits(waveform, bits): diff --git a/test/README.md b/test/README.md index 629e5b54be..62d32b428d 100644 --- a/test/README.md +++ b/test/README.md @@ -1,131 +1,4 @@ # Torchaudio Test Suite -## How to run test - -You can use `pytest` to run `torchaudio`'s test suites. See https://docs.pytest.org/ for the detail of how to use `pytest` command. - -``` -# List up all the tests -pytest test --collect-only -# Run all the test suites -pytest test -# Run tests on sox_effects module -pytest test/sox_effect -# use -k to apply filter -pytest test/sox_io_backend -k load # only runs tests where their names contain load -# Some other useful options; -# Stop on the first failure -x -# Run failure fast --ff -# Only rerun the failure --lf -``` - -**Note** -We use PyTorch's test utilities instead of `pytest` frameworks when writing tests to avoid reinventing the wheel for Tensor comparison. - -## Structure of tests - -The following is an overview of the tests and related modules for `torchaudio`. - -### Purpose specific test suites - -#### Numerical compatibility agains existing software -- [Librosa compatibility test](./torchaudio_unittest/librosa_compatibility_test.py) - Test suite for numerical compatibility against librosa. -- [SoX compatibility test](./torchaudio_unittest/sox_compatibility_test.py) - Test suite for numerical compatibility against SoX. -- [Kaldi compatibility test](./torchaudio_unittest/kaldi_compatibility_test.py) - Test suite for numerical compatibility against Kaldi. - -#### Result consistency with PyTorch framework -- [TorchScript consistency test](./torchaudio_unittest/torchscript_consistency_impl.py) - Test suite to check 1. if an API is TorchScript-able, and 2. the results from Python and Torchscript match. -- [Batch consistency test](./torchaudio_unittest/batch_consistency_test.py) - Test suite to check if functionals/Transforms handle single sample input and batch input and return the same result. - -### Module specific test suites - -The following test modules are defined for corresponding `torchaudio` module/functions. - -- [`torchaudio.datasets`](./torchaudio_unittest/datasets) -- [`torchaudio.functional`](./torchaudio_unittest/functional) -- [`torchaudio.transforms`](./torchaudio_unittest/transforms_test.py) -- [`torchaudio.compliance.kaldi`](./torchaudio_unittest/compliance_kaldi_test.py) -- [`torchaudio.kaldi_io`](./torchaudio_unittest/kaldi_io_test.py) -- [`torchaudio.sox_effects`](test/sox_effects) -- [`torchaudio.save`, `torchaudio.load`, `torchaudio.info`](./torchaudio_unittest/io_test.py) - -### Test modules that do not fall into the above categories -- [test_dataloader.py](./torchaudio_unittest/dataloader_test.py) - Simple test for loading data and applying preprocessing. - -### Support files -- [assets](./torchaudio_unittest/assets): Contain sample audio files. -- [assets/kaldi](./torchaudio_unittest/assets/kaldi): Contains Kaldi format matrix files used in [./torchaudio_unittest/test_compliance_kaldi.py](./torchaudio_unittest/test_compliance_kaldi.py). -- [compliance](./torchaudio_unittest/compliance): Scripts used to generate above Kaldi matrix files. - -### Waveforms for Testing Purposes - -When testing transforms we often need waveforms of specific type (ex: pure tone, noise, or voice), with specific bitrate (ex. 8 or 16 kHz) and number of channels (ex. mono, stereo). Below are some tips on how to construct waveforms and guidance around existing audio files. - -#### Load a Waveform from a File - -```python -filepath = common_utils.get_asset_path('filename.wav') -waveform, sample_rate = common_utils.load_wav(filepath) -``` - -*Note: Should you choose to contribute an audio file, please leave a comment in the issue or pull request, mentioning content source and licensing information. WAV files are preferred. Other formats should be used only when there is no alternative. (i.e. dataset implementation comes with hardcoded non-wav extension).* - -#### Pure Tone - -Code: - -```python -waveform = common_utils.get_sinusoid( - frequency=300, - sample_rate=16000, - duration=1, # seconds - n_channels=1, - dtype="float32", - device="cpu", -) -``` - -#### Noise - -Code: - -```python -tensor = common_utils.get_whitenoise() -``` - -Files: - -* `steam-train-whistle-daniel_simon.wav` - -#### Voice - -Files: - -* `CommonVoice/cv-corpus-4-2019-12-10/tt/clips/common_voice_tt_00000000.wav` -* `VCTK-Corpus/wav48/p224/p224_002.wav` -* `vad-go-stereo-44100.wav` -* `vad-go-mono-32000.wav` - -## Adding test - -The following is the current practice of torchaudio test suite. - -1. Unless the tests are related to I/O, use synthetic data. [`common_utils`](./torchaudio_unittest/common_utils) has some data generator functions. -1. When you add a new test case, use `common_utils.TorchaudioTestCase` as base class unless you are writing tests that are common to CPU / CUDA. - - Set class memeber `dtype`, `device` and `backend` for the desired behavior. - - If you do not set `backend` value in your test suite, then I/O functions will be unassigned and attempt to load/save file will fail. - - For `backend` value, in addition to available backends, you can also provide the value "default" and backend will be picked automatically based on availability. -1. If you are writing tests that should pass on diffrent dtype/devices, write a common class inheriting `common_utils.TestBaseMixin`, then inherit `common_utils.PytorchTestCase` and define class attributes (`dtype` / `device` / `backend`) there. See [Torchscript consistency test implementation](./torchaudio_unittest/torchscript_consistency_impl.py) and test definitions for [CPU](./torchaudio_unittest/torchscript_consistency_cpu_test.py) and [CUDA](./torchaudio_unittest/torchscript_consistency_cuda_test.py) devices. -1. For numerically comparing Tensors, use `assertEqual` method from torchaudio_unittest.common_utils.PytorchTestCase` class. This method has a better support for a wide variety of Tensor types. - -When you add a new feature(functional/transform), consider the following - -1. When you add a new feature, please make it Torchscript-able and batch-consistent unless it degrades the performance. Please add the tests to see if the new feature meet these requirements. -1. If the feature should be numerical compatible against existing software (SoX, Librosa, Kaldi etc), add a corresponding test. -1. If the new feature is unique to `torchaudio` (not a PyTorch implementation of an existing Software functionality), consider adding correctness tests (wheather the expected output is produced for the set of input) under the corresponding test module (`test_functional.py`, `test_transforms.py`). +- [Unit Test](./torchaudio_unittest/) +- [Torchscript Backward Compatibility Test](./torchscript_bc_test/) \ No newline at end of file diff --git a/test/torchaudio_unittest/README.md b/test/torchaudio_unittest/README.md new file mode 100644 index 0000000000..306ca23098 --- /dev/null +++ b/test/torchaudio_unittest/README.md @@ -0,0 +1,131 @@ +# Torchaudio Unit Test Suite + +## How to run test + +You can use `pytest` to run `torchaudio`'s test suites. See https://docs.pytest.org/ for the detail of how to use `pytest` command. + +``` +# List up all the tests +pytest test --collect-only +# Run all the test suites +pytest test +# Run tests on sox_effects module +pytest test/sox_effect +# use -k to apply filter +pytest test/sox_io_backend -k load # only runs tests where their names contain load +# Some other useful options; +# Stop on the first failure -x +# Run failure fast --ff +# Only rerun the failure --lf +``` + +**Note** +We use PyTorch's test utilities instead of `pytest` frameworks when writing tests to avoid reinventing the wheel for Tensor comparison. + +## Structure of tests + +The following is an overview of the tests and related modules for `torchaudio`. + +### Purpose specific test suites + +#### Numerical compatibility agains existing software +- [Librosa compatibility test](./librosa_compatibility_test.py) + Test suite for numerical compatibility against librosa. +- [SoX compatibility test](./sox_compatibility_test.py) + Test suite for numerical compatibility against SoX. +- [Kaldi compatibility test](./kaldi_compatibility_test.py) + Test suite for numerical compatibility against Kaldi. + +#### Result consistency with PyTorch framework +- [TorchScript consistency test](./torchscript_consistency_impl.py) + Test suite to check 1. if an API is TorchScript-able, and 2. the results from Python and Torchscript match. +- [Batch consistency test](./batch_consistency_test.py) + Test suite to check if functionals/Transforms handle single sample input and batch input and return the same result. + +### Module specific test suites + +The following test modules are defined for corresponding `torchaudio` module/functions. + +- [`torchaudio.datasets`](./datasets) +- [`torchaudio.functional`](./functional) +- [`torchaudio.transforms`](./transforms_test.py) +- [`torchaudio.compliance.kaldi`](./compliance_kaldi_test.py) +- [`torchaudio.kaldi_io`](./kaldi_io_test.py) +- [`torchaudio.sox_effects`](test/sox_effects) +- [`torchaudio.save`, `torchaudio.load`, `torchaudio.info`](./io_test.py) + +### Test modules that do not fall into the above categories +- [test_dataloader.py](./dataloader_test.py) + Simple test for loading data and applying preprocessing. + +### Support files +- [assets](./assets): Contain sample audio files. +- [assets/kaldi](./assets/kaldi): Contains Kaldi format matrix files used in [./test_compliance_kaldi.py](./test_compliance_kaldi.py). +- [compliance](./compliance): Scripts used to generate above Kaldi matrix files. + +### Waveforms for Testing Purposes + +When testing transforms we often need waveforms of specific type (ex: pure tone, noise, or voice), with specific bitrate (ex. 8 or 16 kHz) and number of channels (ex. mono, stereo). Below are some tips on how to construct waveforms and guidance around existing audio files. + +#### Load a Waveform from a File + +```python +filepath = common_utils.get_asset_path('filename.wav') +waveform, sample_rate = common_utils.load_wav(filepath) +``` + +*Note: Should you choose to contribute an audio file, please leave a comment in the issue or pull request, mentioning content source and licensing information. WAV files are preferred. Other formats should be used only when there is no alternative. (i.e. dataset implementation comes with hardcoded non-wav extension).* + +#### Pure Tone + +Code: + +```python +waveform = common_utils.get_sinusoid( + frequency=300, + sample_rate=16000, + duration=1, # seconds + n_channels=1, + dtype="float32", + device="cpu", +) +``` + +#### Noise + +Code: + +```python +tensor = common_utils.get_whitenoise() +``` + +Files: + +* `steam-train-whistle-daniel_simon.wav` + +#### Voice + +Files: + +* `CommonVoice/cv-corpus-4-2019-12-10/tt/clips/common_voice_tt_00000000.wav` +* `VCTK-Corpus/wav48/p224/p224_002.wav` +* `vad-go-stereo-44100.wav` +* `vad-go-mono-32000.wav` + +## Adding test + +The following is the current practice of torchaudio test suite. + +1. Unless the tests are related to I/O, use synthetic data. [`common_utils`](./common_utils) has some data generator functions. +1. When you add a new test case, use `common_utils.TorchaudioTestCase` as base class unless you are writing tests that are common to CPU / CUDA. + - Set class memeber `dtype`, `device` and `backend` for the desired behavior. + - If you do not set `backend` value in your test suite, then I/O functions will be unassigned and attempt to load/save file will fail. + - For `backend` value, in addition to available backends, you can also provide the value "default" and backend will be picked automatically based on availability. +1. If you are writing tests that should pass on diffrent dtype/devices, write a common class inheriting `common_utils.TestBaseMixin`, then inherit `common_utils.PytorchTestCase` and define class attributes (`dtype` / `device` / `backend`) there. See [Torchscript consistency test implementation](./torchscript_consistency_impl.py) and test definitions for [CPU](./torchscript_consistency_cpu_test.py) and [CUDA](./torchscript_consistency_cuda_test.py) devices. +1. For numerically comparing Tensors, use `assertEqual` method from torchaudio_unittest.common_utils.PytorchTestCase` class. This method has a better support for a wide variety of Tensor types. + +When you add a new feature(functional/transform), consider the following + +1. When you add a new feature, please make it Torchscript-able and batch-consistent unless it degrades the performance. Please add the tests to see if the new feature meet these requirements. +1. If the feature should be numerical compatible against existing software (SoX, Librosa, Kaldi etc), add a corresponding test. +1. If the new feature is unique to `torchaudio` (not a PyTorch implementation of an existing Software functionality), consider adding correctness tests (wheather the expected output is produced for the set of input) under the corresponding test module (`test_functional.py`, `test_transforms.py`). diff --git a/test/torchaudio_unittest/datasets/vctk_test.py b/test/torchaudio_unittest/datasets/vctk_test.py new file mode 100644 index 0000000000..26b14f5d04 --- /dev/null +++ b/test/torchaudio_unittest/datasets/vctk_test.py @@ -0,0 +1,91 @@ +import os + +from torchaudio.datasets import vctk + +from torchaudio_unittest.common_utils import ( + TempDirMixin, + TorchaudioTestCase, + get_whitenoise, + save_wav, + normalize_wav, +) + +# Used to generate a unique utterance for each dummy audio file +UTTERANCE = [ + 'Please call Stella', + 'Ask her to bring these things', + 'with her from the store', + 'Six spoons of fresh snow peas, five thick slabs of blue cheese, and maybe a snack for her brother Bob', + 'We also need a small plastic snake and a big toy frog for the kids', + 'She can scoop these things into three red bags, and we will go meet her Wednesday at the train station', + 'When the sunlight strikes raindrops in the air, they act as a prism and form a rainbow', + 'The rainbow is a division of white light into many beautiful colors', + 'These take the shape of a long round arch, with its path high above, and its two ends \ + apparently beyond the horizon', + 'There is, according to legend, a boiling pot of gold at one end' +] + + +class TestVCTK(TempDirMixin, TorchaudioTestCase): + backend = 'default' + + root_dir = None + samples = [] + + @classmethod + def setUpClass(cls): + cls.root_dir = cls.get_base_temp_dir() + dataset_dir = os.path.join(cls.root_dir, 'VCTK-Corpus-0.92') + os.makedirs(dataset_dir, exist_ok=True) + sample_rate = 48000 + seed = 0 + + for speaker in range(225, 230): + speaker_id = 'p' + str(speaker) + audio_dir = os.path.join(dataset_dir, 'wav48_silence_trimmed', speaker_id) + os.makedirs(audio_dir, exist_ok=True) + + file_dir = os.path.join(dataset_dir, 'txt', speaker_id) + os.makedirs(file_dir, exist_ok=True) + + for utterance_id in range(1, 11): + filename = f'{speaker_id}_{utterance_id:03d}_mic2' + audio_file_path = os.path.join(audio_dir, filename + '.wav') + + data = get_whitenoise( + sample_rate=sample_rate, + duration=0.01, + n_channels=1, + dtype='float32', + seed=seed + ) + save_wav(audio_file_path, data, sample_rate) + + txt_file_path = os.path.join(file_dir, filename[:-5] + '.txt') + utterance = UTTERANCE[utterance_id - 1] + with open(txt_file_path, 'w') as f: + f.write(utterance) + + sample = ( + normalize_wav(data), + sample_rate, + utterance, + speaker_id, + utterance_id + ) + cls.samples.append(sample) + + seed += 1 + + def test_vctk(self): + dataset = vctk.VCTK_092(self.root_dir, audio_ext=".wav") + num_samples = 0 + for i, (data, sample_rate, utterance, speaker_id, utterance_id) in enumerate(dataset): + self.assertEqual(data, self.samples[i][0], atol=5e-5, rtol=1e-8) + assert sample_rate == self.samples[i][1] + assert utterance == self.samples[i][2] + assert speaker_id == self.samples[i][3] + assert int(utterance_id) == self.samples[i][4] + num_samples += 1 + + assert num_samples == len(self.samples) diff --git a/test/torchaudio_unittest/sox_io_backend/info_test.py b/test/torchaudio_unittest/sox_io_backend/info_test.py index f07b72e112..b16203cfd3 100644 --- a/test/torchaudio_unittest/sox_io_backend/info_test.py +++ b/test/torchaudio_unittest/sox_io_backend/info_test.py @@ -108,6 +108,20 @@ def test_vorbis(self, sample_rate, num_channels, quality_level): assert info.num_frames == sample_rate * duration assert info.num_channels == num_channels + @parameterized.expand(list(itertools.product( + [8000, 16000], + [1, 2], + )), name_func=name_func) + def test_sphere(self, sample_rate, num_channels): + """`sox_io_backend.info` can check sph file correctly""" + duration = 1 + path = self.get_temp_path('data.sph') + sox_utils.gen_audio_file(path, sample_rate, num_channels, duration=duration) + info = sox_io_backend.info(path) + assert info.sample_rate == sample_rate + assert info.num_frames == sample_rate * duration + assert info.num_channels == num_channels + @skipIfNoExtension class TestInfoOpus(PytorchTestCase): diff --git a/test/torchaudio_unittest/sox_io_backend/load_test.py b/test/torchaudio_unittest/sox_io_backend/load_test.py index 1ff8b76dc5..59bcaea386 100644 --- a/test/torchaudio_unittest/sox_io_backend/load_test.py +++ b/test/torchaudio_unittest/sox_io_backend/load_test.py @@ -120,6 +120,28 @@ def assert_vorbis(self, sample_rate, num_channels, quality_level, duration): assert sr == sample_rate self.assertEqual(data, data_ref, atol=4e-05, rtol=1.3e-06) + def assert_sphere(self, sample_rate, num_channels, duration): + """`sox_io_backend.load` can load sph format. + + This test takes the same strategy as mp3 to compare the result + """ + path = self.get_temp_path('1.original.sph') + ref_path = self.get_temp_path('2.reference.wav') + + # 1. Generate sph with sox + sox_utils.gen_audio_file( + path, sample_rate, num_channels, + bit_depth=32, duration=duration) + # 2. Convert to wav with sox + sox_utils.convert_audio_file(path, ref_path) + # 3. Load sph with torchaudio + data, sr = sox_io_backend.load(path) + # 4. Load wav with scipy + data_ref = load_wav(ref_path)[0] + # 5. Compare + assert sr == sample_rate + self.assertEqual(data, data_ref, atol=4e-05, rtol=1.3e-06) + @skipIfNoExec('sox') @skipIfNoExtension @@ -230,6 +252,14 @@ def test_opus(self, bitrate, num_channels, compression_level): assert sample_rate == sr self.assertEqual(expected, found) + @parameterized.expand(list(itertools.product( + [8000, 16000], + [1, 2], + )), name_func=name_func) + def test_sphere(self, sample_rate, num_channels): + """`sox_io_backend.load` can load sph format correctly.""" + self.assert_sphere(sample_rate, num_channels, duration=1) + @skipIfNoExec('sox') @skipIfNoExtension diff --git a/test/torchaudio_unittest/sox_io_backend/save_test.py b/test/torchaudio_unittest/sox_io_backend/save_test.py index 8e416b7aa9..f7d4da3931 100644 --- a/test/torchaudio_unittest/sox_io_backend/save_test.py +++ b/test/torchaudio_unittest/sox_io_backend/save_test.py @@ -168,6 +168,38 @@ def assert_vorbis(self, *args, **kwargs): else: raise error + def assert_sphere(self, sample_rate, num_channels, duration): + """`sox_io_backend.save` can save sph format. + + This test takes the same strategy as mp3 to compare the result + """ + src_path = self.get_temp_path('1.reference.wav') + flc_path = self.get_temp_path('2.1.torchaudio.sph') + wav_path = self.get_temp_path('2.2.torchaudio.wav') + flc_path_sox = self.get_temp_path('3.1.sox.sph') + wav_path_sox = self.get_temp_path('3.2.sox.wav') + + # 1. Generate original wav + data = get_wav_data('float32', num_channels, normalize=True, num_frames=duration * sample_rate) + save_wav(src_path, data, sample_rate) + # 2.1. Convert the original wav to sph with torchaudio + sox_io_backend.save(flc_path, load_wav(src_path)[0], sample_rate) + # 2.2. Convert the sph to wav with Sox + # converting to 32 bit because sph file has 24 bit depth which scipy cannot handle. + sox_utils.convert_audio_file(flc_path, wav_path, bit_depth=32) + # 2.3. Load + found = load_wav(wav_path)[0] + + # 3.1. Convert the original wav to sph with SoX + sox_utils.convert_audio_file(src_path, flc_path_sox) + # 3.2. Convert the sph to wav with Sox + # converting to 32 bit because sph file has 24 bit depth which scipy cannot handle. + sox_utils.convert_audio_file(flc_path_sox, wav_path_sox, bit_depth=32) + # 3.3. Load + expected = load_wav(wav_path_sox)[0] + + self.assertEqual(found, expected) + @skipIfNoExec('sox') @skipIfNoExtension @@ -262,6 +294,14 @@ def test_vorbis_large(self, sample_rate, num_channels, quality_level): self.assert_vorbis(sample_rate, num_channels, quality_level, two_hours) ''' + @parameterized.expand(list(itertools.product( + [8000, 16000], + [1, 2], + )), name_func=name_func) + def test_sphere(self, sample_rate, num_channels): + """`sox_io_backend.save` can save sph format.""" + self.assert_sphere(sample_rate, num_channels, duration=1) + @skipIfNoExec('sox') @skipIfNoExtension diff --git a/test/torchaudio_unittest/torchscript_consistency_impl.py b/test/torchaudio_unittest/torchscript_consistency_impl.py index 8d14e538fd..82c6a300a6 100644 --- a/test/torchaudio_unittest/torchscript_consistency_impl.py +++ b/test/torchaudio_unittest/torchscript_consistency_impl.py @@ -194,6 +194,13 @@ def func(tensor): tensor = common_utils.get_whitenoise(n_channels=2) self._assert_consistency(func, tensor, shape_only=True) + def test_dither_noise_shaping(self): + def func(tensor): + return F.dither(tensor, noise_shaping=True) + + tensor = common_utils.get_whitenoise(n_channels=2) + self._assert_consistency(func, tensor) + def test_lfilter(self): if self.dtype == torch.float64: raise unittest.SkipTest("This test is known to fail for float64") diff --git a/test/torchscript_bc_test/README.md b/test/torchscript_bc_test/README.md new file mode 100644 index 0000000000..4cf0e2b226 --- /dev/null +++ b/test/torchscript_bc_test/README.md @@ -0,0 +1,23 @@ +# Torchscript Backward Compatibility Test Suite + +This directory contains tools to generate Torchscript object of a specific torchaudio version (given that you have the corresponding environments setup correctly) and validate it in the current version. + +## Usage + +### Generate torchscript object + +``` +./main.py --mode generate --version 0.6.0 +``` + +will generate Torchscript dump files in [`assets`](./assets/) directory. This requries your Python runtime to have torchaudio `0.6.0`. + + +### Validate torchscript object + + +``` +./main.py --mode validate --version 0.6.0 +``` + +will validate if the Torchscript files created in the previous step are compatible with the version of torchaudio available in your environment (master). diff --git a/test/torchscript_bc_test/main.py b/test/torchscript_bc_test/main.py new file mode 100755 index 0000000000..3c1c8d974e --- /dev/null +++ b/test/torchscript_bc_test/main.py @@ -0,0 +1,71 @@ +#!/usr/bin/env python3 +"""Generate torchscript object of specific torhcaudio version. + +This requires that the corresponding torchaudio (and torch) is installed. +""" +import os +import sys +import argparse + + +_BASE_OBJ_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'assets') + + +def _parse_args(): + parser = argparse.ArgumentParser( + description=__doc__ + ) + parser.add_argument( + '--mode', choices=['generate', 'validate'], required=True, + help=( + '"generate" generates Torchscript objects of the specific torchaudio ' + 'in the given directory. ' + '"validate" validates if the objects in the givcen directory are compatible ' + 'with the current torhcaudio.' + ) + ) + parser.add_argument( + '--version', choices=['0.6.0'], required=True, + help='torchaudio version.' + ) + parser.add_argument( + '--base-obj-dir', default=_BASE_OBJ_DIR, + help='Directory where objects are saved/loaded.' + ) + return parser.parse_args() + + +def _generate(version, output_dir): + if version == '0.6.0': + import ver_060 + ver_060.generate(output_dir) + else: + raise ValueError(f'Unexpected torchaudio version: {version}') + + +def _validate(version, input_dir): + if version == '0.6.0': + import ver_060 + ver_060.validate(input_dir) + else: + raise ValueError(f'Unexpected torchaudio version: {version}') + + +def _get_obj_dir(base_dir, version): + py_version = f'{sys.version_info.major}.{sys.version_info.minor}' + return os.path.join(base_dir, f'{version}-py{py_version}') + + +def _main(): + args = _parse_args() + obj_dir = _get_obj_dir(args.base_obj_dir, args.version) + if args.mode == 'generate': + _generate(args.version, obj_dir) + elif args.mode == 'validate': + _validate(args.version, obj_dir) + else: + raise ValueError(f'Unexpected mode: {args.mode}') + + +if __name__ == '__main__': + _main() diff --git a/test/torchscript_bc_test/ver_060.py b/test/torchscript_bc_test/ver_060.py new file mode 100644 index 0000000000..78b8da228c --- /dev/null +++ b/test/torchscript_bc_test/ver_060.py @@ -0,0 +1,71 @@ +import os +import tempfile +from typing import Optional +from packaging import version + +import torch +import torchaudio + +_MIN_VER = version.parse('0.6.0a0') +_MAX_VER = version.parse('0.7.0') +_RUNTIME_VER = version.parse(torchaudio.__version__) + + +def info(filepath: str) -> torchaudio.backend.sox_io_backend.AudioMetaData: + return torchaudio.info(filepath) + + +def load( + filepath: str, + frame_offset: int, + num_frames: int, + normalize: bool, + channels_first: bool): + return torchaudio.load(filepath, frame_offset, num_frames, normalize, channels_first) + + +def save( + filepath: str, + tensor: torch.Tensor, + sample_rate: int, + channels_first: bool = True, + compression: Optional[float] = None, +): + torchaudio.save(filepath, tensor, sample_rate, channels_first, compression) + + +def generate(output_dir): + if not (_MIN_VER <= _RUNTIME_VER < _MAX_VER): + raise RuntimeError(f'Invalid torchaudio runtime version: {_RUNTIME_VER}') + + torchaudio.set_audio_backend('sox_io') + + funcs = [ + info, + load, + save, + ] + + os.makedirs(output_dir, exist_ok=True) + for func in funcs: + torch.jit.script(func).save(os.path.join(output_dir, f'{func.__name__}.zip')) + + +def validate(input_dir): + torchaudio.set_audio_backend('sox_io') + + # See https://github.com/pytorch/pytorch/issues/42258 + # info_ = torch.jit.load(os.path.join(input_dir, 'info.zip')) + load_ = torch.jit.load(os.path.join(input_dir, 'load.zip')) + save_ = torch.jit.load(os.path.join(input_dir, 'save.zip')) + + sample_rate = 44100 + normalize = True + channels_first = True + with tempfile.TemporaryDirectory() as temp_dir: + temp_file = os.path.join(temp_dir, 'test.wav') + temp_data = torch.rand(2, sample_rate, dtype=torch.float32) + + save_(temp_file, temp_data, sample_rate, channels_first, 0.) + # info_(temp_file) + load_(temp_file, 0, -1, normalize, channels_first) diff --git a/torchaudio/backend/sox_io_backend.py b/torchaudio/backend/sox_io_backend.py index 57d8db7723..9c0695d723 100644 --- a/torchaudio/backend/sox_io_backend.py +++ b/torchaudio/backend/sox_io_backend.py @@ -58,6 +58,7 @@ def load( * FLAC * OGG/VORBIS * OPUS + * SPHERE To load ``MP3``, ``FLAC``, ``OGG/VORBIS``, ``OPUS`` and other codecs ``libsox`` does not handle natively, your installation of ``torchaudio`` has to be linked to ``libsox`` @@ -132,6 +133,7 @@ def save( * MP3 * FLAC * OGG/VORBIS + * SPHERE To save ``MP3``, ``FLAC``, ``OGG/VORBIS``, and other codecs ``libsox`` does not handle natively, your installation of ``torchaudio`` has to be linked to ``libsox`` @@ -158,7 +160,7 @@ def save( """ if compression is None: ext = str(filepath)[-3:].lower() - if ext == 'wav': + if ext in ['wav', 'sph']: compression = 0. elif ext == 'mp3': compression = -4.5 diff --git a/torchaudio/csrc/register.cpp b/torchaudio/csrc/register.cpp index 476dcc3ebb..a1ec8f2254 100644 --- a/torchaudio/csrc/register.cpp +++ b/torchaudio/csrc/register.cpp @@ -5,86 +5,70 @@ #include #include -namespace torchaudio { -namespace { +TORCH_LIBRARY(torchaudio, m) { + ////////////////////////////////////////////////////////////////////////////// + // sox_utils.h + ////////////////////////////////////////////////////////////////////////////// + m.class_("TensorSignal") + .def(torch::init()) + .def("get_tensor", &torchaudio::sox_utils::TensorSignal::getTensor) + .def( + "get_sample_rate", + &torchaudio::sox_utils::TensorSignal::getSampleRate) + .def( + "get_channels_first", + &torchaudio::sox_utils::TensorSignal::getChannelsFirst); -//////////////////////////////////////////////////////////////////////////////// -// sox_utils.h -//////////////////////////////////////////////////////////////////////////////// -static auto registerTensorSignal = - torch::class_("torchaudio", "TensorSignal") - .def(torch::init()) - .def("get_tensor", &sox_utils::TensorSignal::getTensor) - .def("get_sample_rate", &sox_utils::TensorSignal::getSampleRate) - .def("get_channels_first", &sox_utils::TensorSignal::getChannelsFirst); + m.def("torchaudio::sox_utils_set_seed", &torchaudio::sox_utils::set_seed); + m.def( + "torchaudio::sox_utils_set_verbosity", + &torchaudio::sox_utils::set_verbosity); + m.def( + "torchaudio::sox_utils_set_use_threads", + &torchaudio::sox_utils::set_use_threads); + m.def( + "torchaudio::sox_utils_set_buffer_size", + &torchaudio::sox_utils::set_buffer_size); + m.def( + "torchaudio::sox_utils_list_effects", + &torchaudio::sox_utils::list_effects); + m.def( + "torchaudio::sox_utils_list_read_formats", + &torchaudio::sox_utils::list_read_formats); + m.def( + "torchaudio::sox_utils_list_write_formats", + &torchaudio::sox_utils::list_write_formats); -static auto registerSetSoxOptions = - torch::RegisterOperators() - .op("torchaudio::sox_utils_set_seed", &sox_utils::set_seed) - .op("torchaudio::sox_utils_set_verbosity", &sox_utils::set_verbosity) - .op("torchaudio::sox_utils_set_use_threads", - &sox_utils::set_use_threads) - .op("torchaudio::sox_utils_set_buffer_size", - &sox_utils::set_buffer_size) - .op("torchaudio::sox_utils_list_effects", &sox_utils::list_effects) - .op("torchaudio::sox_utils_list_read_formats", - &sox_utils::list_read_formats) - .op("torchaudio::sox_utils_list_write_formats", - &sox_utils::list_write_formats); + ////////////////////////////////////////////////////////////////////////////// + // sox_io.h + ////////////////////////////////////////////////////////////////////////////// + m.class_("SignalInfo") + .def("get_sample_rate", &torchaudio::sox_io::SignalInfo::getSampleRate) + .def("get_num_channels", &torchaudio::sox_io::SignalInfo::getNumChannels) + .def("get_num_frames", &torchaudio::sox_io::SignalInfo::getNumFrames); -//////////////////////////////////////////////////////////////////////////////// -// sox_io.h -//////////////////////////////////////////////////////////////////////////////// -static auto registerSignalInfo = - torch::class_("torchaudio", "SignalInfo") - .def("get_sample_rate", &sox_io::SignalInfo::getSampleRate) - .def("get_num_channels", &sox_io::SignalInfo::getNumChannels) - .def("get_num_frames", &sox_io::SignalInfo::getNumFrames); + m.def("torchaudio::sox_io_get_info", &torchaudio::sox_io::get_info); + m.def( + "torchaudio::sox_io_load_audio_file", + &torchaudio::sox_io::load_audio_file); + m.def( + "torchaudio::sox_io_save_audio_file", + &torchaudio::sox_io::save_audio_file); -static auto registerGetInfo = torch::RegisterOperators().op( - torch::RegisterOperators::options() - .schema( - "torchaudio::sox_io_get_info(str path) -> __torch__.torch.classes.torchaudio.SignalInfo info") - .catchAllKernel()); - -static auto registerLoadAudioFile = torch::RegisterOperators().op( - torch::RegisterOperators::options() - .schema( - "torchaudio::sox_io_load_audio_file(str path, int frame_offset, int num_frames, bool normalize, bool channels_first) -> __torch__.torch.classes.torchaudio.TensorSignal signal") - .catchAllKernel< - decltype(sox_io::load_audio_file), - &sox_io::load_audio_file>()); - -static auto registerSaveAudioFile = torch::RegisterOperators().op( - torch::RegisterOperators::options() - .schema( - "torchaudio::sox_io_save_audio_file(str path, __torch__.torch.classes.torchaudio.TensorSignal signal, float compression) -> ()") - .catchAllKernel< - decltype(sox_io::save_audio_file), - &sox_io::save_audio_file>()); - -//////////////////////////////////////////////////////////////////////////////// -// sox_effects.h -//////////////////////////////////////////////////////////////////////////////// -static auto registerSoxEffects = - torch::RegisterOperators() - .op("torchaudio::sox_effects_initialize_sox_effects", - &sox_effects::initialize_sox_effects) - .op("torchaudio::sox_effects_shutdown_sox_effects", - &sox_effects::shutdown_sox_effects) - .op(torch::RegisterOperators::options() - .schema( - "torchaudio::sox_effects_apply_effects_tensor(__torch__.torch.classes.torchaudio.TensorSignal input_signal, str[][] effects) -> __torch__.torch.classes.torchaudio.TensorSignal output_signal") - .catchAllKernel< - decltype(sox_effects::apply_effects_tensor), - &sox_effects::apply_effects_tensor>()) - .op(torch::RegisterOperators::options() - .schema( - "torchaudio::sox_effects_apply_effects_file(str path, str[][] effects, bool normalize, bool channels_first) -> __torch__.torch.classes.torchaudio.TensorSignal output_signal") - .catchAllKernel< - decltype(sox_effects::apply_effects_file), - &sox_effects::apply_effects_file>()); - -} // namespace -} // namespace torchaudio + ////////////////////////////////////////////////////////////////////////////// + // sox_effects.h + ////////////////////////////////////////////////////////////////////////////// + m.def( + "torchaudio::sox_effects_initialize_sox_effects", + &torchaudio::sox_effects::initialize_sox_effects); + m.def( + "torchaudio::sox_effects_shutdown_sox_effects", + &torchaudio::sox_effects::shutdown_sox_effects); + m.def( + "torchaudio::sox_effects_apply_effects_tensor", + &torchaudio::sox_effects::apply_effects_tensor); + m.def( + "torchaudio::sox_effects_apply_effects_file", + &torchaudio::sox_effects::apply_effects_file); +} #endif diff --git a/torchaudio/csrc/sox_utils.cpp b/torchaudio/csrc/sox_utils.cpp index c6b73c5795..25fe376c8b 100644 --- a/torchaudio/csrc/sox_utils.cpp +++ b/torchaudio/csrc/sox_utils.cpp @@ -234,6 +234,8 @@ sox_encoding_t get_encoding( return SOX_ENCODING_FLOAT; throw std::runtime_error("Unsupported dtype."); } + if (filetype == "sph") + return SOX_ENCODING_SIGN2; throw std::runtime_error("Unsupported file type."); } @@ -257,6 +259,8 @@ unsigned get_precision( return 32; throw std::runtime_error("Unsupported dtype."); } + if (filetype == "sph") + return 32; throw std::runtime_error("Unsupported file type."); } @@ -285,6 +289,8 @@ sox_encodinginfo_t get_encodinginfo( return compression; if (filetype == "wav") return 0.; + if (filetype == "sph") + return 0.; throw std::runtime_error("Unsupported file type."); }(); diff --git a/torchaudio/datasets/__init__.py b/torchaudio/datasets/__init__.py index 187142db46..fb9e0db506 100644 --- a/torchaudio/datasets/__init__.py +++ b/torchaudio/datasets/__init__.py @@ -2,7 +2,7 @@ from .librispeech import LIBRISPEECH from .speechcommands import SPEECHCOMMANDS from .utils import bg_iterator, diskcache_iterator -from .vctk import VCTK +from .vctk import VCTK, VCTK_092 from .gtzan import GTZAN from .yesno import YESNO from .ljspeech import LJSPEECH @@ -14,6 +14,7 @@ "LIBRISPEECH", "SPEECHCOMMANDS", "VCTK", + "VCTK_092", "YESNO", "LJSPEECH", "GTZAN", diff --git a/torchaudio/datasets/vctk.py b/torchaudio/datasets/vctk.py index c84373713d..8fa874c7cf 100644 --- a/torchaudio/datasets/vctk.py +++ b/torchaudio/datasets/vctk.py @@ -1,6 +1,7 @@ import os import warnings from typing import Any, Tuple +from collections import namedtuple import torchaudio from torch import Tensor @@ -11,13 +12,16 @@ walk_files ) -URL = "http://homepages.inf.ed.ac.uk/jyamagis/release/VCTK-Corpus.tar.gz" +URL = "https://datashare.is.ed.ac.uk/bitstream/handle/10283/3443/VCTK-Corpus-0.92.zip" FOLDER_IN_ARCHIVE = "VCTK-Corpus" _CHECKSUMS = { - "http://homepages.inf.ed.ac.uk/jyamagis/release/VCTK-Corpus.tar.gz": - "45e8dede780278ef5541fde0b82ac292" + "https://datashare.is.ed.ac.uk/bitstream/handle/10283/3443/VCTK-Corpus-0.92.zip": "8a6ba2946b36fcbef0212cad601f4bfa" } +Sample = namedtuple( + "Sample", ["waveform", "sample_rate", "utterance", "speaker_id", "utterance_id"] +) + def load_vctk_item(fileid: str, path: str, @@ -96,15 +100,15 @@ def __init__(self, self._path = os.path.join(root, folder_in_archive) if download: - if not os.path.isdir(self._path): - if not os.path.isfile(archive): - checksum = _CHECKSUMS.get(url, None) - download_url(url, root, hash_value=checksum, hash_type="md5") - extract_archive(archive) + raise RuntimeError( + "This Dataset is no longer available. " + "Please use `VCTK_092` class to download the latest version." + ) if not os.path.isdir(self._path): raise RuntimeError( - "Dataset not found. Please use `download=True` to download it." + "Dataset not found. Please use `VCTK_092` class " + "with `download=True` to donwload the latest version." ) walker = walk_files( @@ -136,3 +140,120 @@ def __getitem__(self, n: int) -> Tuple[Tensor, int, str, str, str]: def __len__(self) -> int: return len(self._walker) + + +class VCTK_092(Dataset): + """Create VCTK 0.92 Dataset + + An item is a ``namedtuple`` of (``waveform``, ``sample_rate``, ``utterance``, + ``speaker_id``, ``utterance_id``) + + Args: + root (str): Root directory where the dataset's top level directory is found. + mic_id (str): Microphone ID. Either ``"mic1"`` or ``"mic2"`` + download (bool, optional): Download the dataset if not found in the given directory. + url (str, optional): URL from which the dataset is downloaded. + audio_ext (str, optional): Custom audio extension if dataset is converted to non-default audio format. + + Note: + * All the speeches from speaker ``p315`` will be skipped due to the lack of the corresponding text files. + * All the speeches from ``p280`` will be skipped for ``mic_id="mic2"`` due to the lack of the audio files. + * Some of the speeches from speaker ``p362`` will be skipped due to the lack of the audio files. + * See Also: https://datashare.is.ed.ac.uk/handle/10283/3443 + """ + + def __init__( + self, + root: str, + mic_id: str = "mic2", + download: bool = False, + url: str = URL, + audio_ext=".flac", + ): + if mic_id not in ["mic1", "mic2"]: + raise RuntimeError( + f'`mic_id` has to be either "mic1" or "mic2". Found: {mic_id}' + ) + + archive = os.path.join(root, "VCTK-Corpus-0.92.zip") + + self._path = os.path.join(root, "VCTK-Corpus-0.92") + self._txt_dir = os.path.join(self._path, "txt") + self._audio_dir = os.path.join(self._path, "wav48_silence_trimmed") + self._mic_id = mic_id + self._audio_ext = audio_ext + + if download: + if not os.path.isdir(self._path): + if not os.path.isfile(archive): + checksum = _CHECKSUMS.get(url, None) + download_url(url, root, hash_value=checksum, hash_type="md5") + extract_archive(archive, self._path) + + if not os.path.isdir(self._path): + raise RuntimeError( + "Dataset not found. Please use `download=True` to download it." + ) + + # Extracting speaker IDs from the folder structure + self._speaker_ids = sorted(os.listdir(self._txt_dir)) + self._sample_ids = [] + + """ + Due to some insufficient data complexity in the 0.92 version of this dataset, + we start traversing the audio folder structure in accordance with the text folder. + As some of the audio files are missing of either ``mic_1`` or ``mic_2`` but the + text is present for the same, we first check for the existence of the audio file + before adding it to the ``sample_ids`` list. + + Once the ``audio_ids`` are loaded into memory we can quickly access the list for + different parameters required by the user. + """ + for speaker_id in self._speaker_ids: + if speaker_id == "p280" and mic_id == "mic2": + continue + utterance_dir = os.path.join(self._txt_dir, speaker_id) + for utterance_file in sorted( + f for f in os.listdir(utterance_dir) if f.endswith(".txt") + ): + utterance_id = os.path.splitext(utterance_file)[0] + audio_path_mic = os.path.join( + self._audio_dir, + speaker_id, + f"{utterance_id}_{mic_id}{self._audio_ext}", + ) + if speaker_id == "p362" and not os.path.isfile(audio_path_mic): + continue + self._sample_ids.append(utterance_id.split("_")) + + def _load_text(self, file_path) -> str: + with open(file_path) as file_path: + return file_path.readlines()[0] + + def _load_audio(self, file_path) -> Tuple[Tensor, int]: + return torchaudio.load(file_path) + + def _load_sample(self, speaker_id: str, utterance_id: str, mic_id: str) -> Sample: + utterance_path = os.path.join( + self._txt_dir, speaker_id, f"{speaker_id}_{utterance_id}.txt" + ) + audio_path = os.path.join( + self._audio_dir, + speaker_id, + f"{speaker_id}_{utterance_id}_{mic_id}{self._audio_ext}", + ) + + # Reading text + utterance = self._load_text(utterance_path) + + # Reading FLAC + waveform, sample_rate = self._load_audio(audio_path) + + return Sample(waveform, sample_rate, utterance, speaker_id, utterance_id) + + def __getitem__(self, n: int) -> Sample: + speaker_id, utterance_id = self._sample_ids[n] + return self._load_sample(speaker_id, utterance_id, self._mic_id) + + def __len__(self) -> int: + return len(self._sample_ids) diff --git a/torchaudio/functional.py b/torchaudio/functional.py index 4b79c01960..4e8e62848e 100644 --- a/torchaudio/functional.py +++ b/torchaudio/functional.py @@ -1655,9 +1655,10 @@ def _add_noise_shaping( error = dithered_waveform - waveform # add error[n-1] to dithered_waveform[n], so offset the error by 1 index + zeros = torch.zeros(1, dtype=error.dtype, device=error.device) for index in range(error.size()[0]): err = error[index] - error_offset = torch.cat((torch.zeros(1), err)) + error_offset = torch.cat((zeros, err)) error[index] = error_offset[:waveform.size()[1]] noise_shaped = dithered_waveform + error From 7d5591f5b90c3fe70902fede6af157ea92e3206a Mon Sep 17 00:00:00 2001 From: Moto Hira Date: Wed, 23 Sep 2020 08:59:35 -0700 Subject: [PATCH 18/19] Import torchaudio 20200922 Summary: Up to #914 Reviewed By: vincentqb, cpuhrsch Differential Revision: D23846718 fbshipit-source-id: 9feb4e58563b900965467bd9ff66c979211c50df --- .circleci/config.yml | 22 +- .circleci/config.yml.in | 22 +- .../unittest/linux/scripts/environment.yml | 1 + docs/source/datasets.rst | 15 +- examples/interactive_asr/utils.py | 8 +- packaging/pkg_helpers.bash | 2 +- .../datasets/tedlium_test.py | 153 +++++++++++++ .../functional/functional_cpu_test.py | 17 ++ .../sox_io_backend/save_test.py | 4 +- torchaudio/backend/soundfile_backend.py | 1 + torchaudio/backend/sox_backend.py | 1 + torchaudio/backend/sox_io_backend.py | 22 +- torchaudio/backend/utils.py | 5 + torchaudio/datasets/__init__.py | 4 +- torchaudio/datasets/tedlium.py | 208 ++++++++++++++++++ torchaudio/functional.py | 7 + 16 files changed, 454 insertions(+), 38 deletions(-) create mode 100644 test/torchaudio_unittest/datasets/tedlium_test.py create mode 100644 torchaudio/datasets/tedlium.py diff --git a/.circleci/config.yml b/.circleci/config.yml index 70b7c54ef5..b2b0dd45c3 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -144,14 +144,14 @@ jobs: - store_artifacts: path: /opt/conda/conda-bld/linux-64 - persist_to_workspace: - root: /opt/conda/conda-bld/linux-64 + root: /opt/conda paths: - - "*" + - "conda-bld/*" binary_macos_wheel: <<: *binary_common macos: - xcode: "9.0" + xcode: "9.4.1" steps: - checkout - install_build_tools_macos @@ -176,7 +176,7 @@ jobs: binary_macos_conda: <<: *binary_common macos: - xcode: "9.0" + xcode: "9.4.1" steps: - checkout - install_build_tools_macos @@ -192,9 +192,9 @@ jobs: - store_artifacts: path: /Users/distiller/miniconda3/conda-bld/osx-64 - persist_to_workspace: - root: /Users/distiller/miniconda3/conda-bld/osx-64 + root: /Users/distiller/miniconda3 paths: - - "*" + - "conda-bld/*" binary_windows_wheel: <<: *binary_common @@ -231,9 +231,9 @@ jobs: - store_artifacts: path: C:/tools/miniconda3/conda-bld/win-64 - persist_to_workspace: - root: C:/tools/miniconda3/conda-bld/win-64 + root: C:/tools/miniconda3 paths: - - "*" + - "conda-bld/*" # Requires org-member context binary_conda_upload: @@ -248,7 +248,7 @@ jobs: # Prevent credential from leaking conda install -yq anaconda-client set -x - anaconda -t "${CONDA_PYTORCHBOT_TOKEN}" upload ~/workspace/*.tar.bz2 -u "pytorch-${UPLOAD_CHANNEL}" --label main --no-progress --force + anaconda -t "${CONDA_PYTORCHBOT_TOKEN}" upload ~/workspace/conda-bld/*/*.tar.bz2 -u "pytorch-${UPLOAD_CHANNEL}" --label main --no-progress --force # Requires org-member context binary_wheel_upload: @@ -284,7 +284,7 @@ jobs: set -x source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION} conda install -v -y -c pytorch-${UPLOAD_CHANNEL} pytorch - conda install -v -y $(ls ~/workspace/torchaudio*.tar.bz2) + conda install -v -y -c file://$HOME/workspace/conda-bld torchaudio - run: name: smoke test command: | @@ -537,7 +537,7 @@ jobs: unittest_macos_cpu: <<: *binary_common macos: - xcode: "9.0" + xcode: "9.4.1" resource_class: large steps: - checkout diff --git a/.circleci/config.yml.in b/.circleci/config.yml.in index 94220d02c0..b1f40c24c0 100644 --- a/.circleci/config.yml.in +++ b/.circleci/config.yml.in @@ -144,14 +144,14 @@ jobs: - store_artifacts: path: /opt/conda/conda-bld/linux-64 - persist_to_workspace: - root: /opt/conda/conda-bld/linux-64 + root: /opt/conda paths: - - "*" + - "conda-bld/*" binary_macos_wheel: <<: *binary_common macos: - xcode: "9.0" + xcode: "9.4.1" steps: - checkout - install_build_tools_macos @@ -176,7 +176,7 @@ jobs: binary_macos_conda: <<: *binary_common macos: - xcode: "9.0" + xcode: "9.4.1" steps: - checkout - install_build_tools_macos @@ -192,9 +192,9 @@ jobs: - store_artifacts: path: /Users/distiller/miniconda3/conda-bld/osx-64 - persist_to_workspace: - root: /Users/distiller/miniconda3/conda-bld/osx-64 + root: /Users/distiller/miniconda3 paths: - - "*" + - "conda-bld/*" binary_windows_wheel: <<: *binary_common @@ -231,9 +231,9 @@ jobs: - store_artifacts: path: C:/tools/miniconda3/conda-bld/win-64 - persist_to_workspace: - root: C:/tools/miniconda3/conda-bld/win-64 + root: C:/tools/miniconda3 paths: - - "*" + - "conda-bld/*" # Requires org-member context binary_conda_upload: @@ -248,7 +248,7 @@ jobs: # Prevent credential from leaking conda install -yq anaconda-client set -x - anaconda -t "${CONDA_PYTORCHBOT_TOKEN}" upload ~/workspace/*.tar.bz2 -u "pytorch-${UPLOAD_CHANNEL}" --label main --no-progress --force + anaconda -t "${CONDA_PYTORCHBOT_TOKEN}" upload ~/workspace/conda-bld/*/*.tar.bz2 -u "pytorch-${UPLOAD_CHANNEL}" --label main --no-progress --force # Requires org-member context binary_wheel_upload: @@ -284,7 +284,7 @@ jobs: set -x source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION} conda install -v -y -c pytorch-${UPLOAD_CHANNEL} pytorch - conda install -v -y $(ls ~/workspace/torchaudio*.tar.bz2) + conda install -v -y -c file://$HOME/workspace/conda-bld torchaudio - run: name: smoke test command: | @@ -537,7 +537,7 @@ jobs: unittest_macos_cpu: <<: *binary_common macos: - xcode: "9.0" + xcode: "9.4.1" resource_class: large steps: - checkout diff --git a/.circleci/unittest/linux/scripts/environment.yml b/.circleci/unittest/linux/scripts/environment.yml index f68fb130a2..42acafb984 100644 --- a/.circleci/unittest/linux/scripts/environment.yml +++ b/.circleci/unittest/linux/scripts/environment.yml @@ -1,3 +1,4 @@ +# a comment for these trying times channels: - conda-forge - defaults diff --git a/docs/source/datasets.rst b/docs/source/datasets.rst index 6cdf44e223..b3f1b3c725 100644 --- a/docs/source/datasets.rst +++ b/docs/source/datasets.rst @@ -4,11 +4,11 @@ torchaudio.datasets All datasets are subclasses of :class:`torch.utils.data.Dataset` i.e, they have ``__getitem__`` and ``__len__`` methods implemented. Hence, they can all be passed to a :class:`torch.utils.data.DataLoader` -which can load multiple samples parallelly using ``torch.multiprocessing`` workers. +which can load multiple samples parallelly using ``torch.multiprocessing`` workers. For example: :: - + yesno_data = torchaudio.datasets.YESNO('.', download=True) - data_loader = torch.utils.data.DataLoader(yesno_data, + data_loader = torch.utils.data.DataLoader(yesno_data, batch_size=1, shuffle=True, num_workers=args.nThreads) @@ -22,7 +22,7 @@ All the datasets have almost similar API. They all have two common arguments: ``transform`` and ``target_transform`` to transform the input and target respectively. -.. currentmodule:: torchaudio.datasets +.. currentmodule:: torchaudio.datasets CMUARCTIC @@ -81,6 +81,13 @@ SPEECHCOMMANDS :special-members: +TEDLIUM +~~~~~~~~~~~~~~ + +.. autoclass:: TEDLIUM + :members: __getitem__ + :special-members: get_phoneme_dict + VCTK ~~~~ diff --git a/examples/interactive_asr/utils.py b/examples/interactive_asr/utils.py index 713954029a..37f5ae5303 100644 --- a/examples/interactive_asr/utils.py +++ b/examples/interactive_asr/utils.py @@ -55,8 +55,9 @@ def check_args(args): def process_predictions(args, hypos, sp, tgt_dict): res = [] + device = torch.device("cuda:0" if torch.cuda.is_available() and not args.cpu else "cpu") for hypo in hypos[: min(len(hypos), args.nbest)]: - hyp_pieces = tgt_dict.string(hypo["tokens"].int().cpu()) + hyp_pieces = tgt_dict.string(hypo["tokens"].int().to(device)) hyp_words = sp.DecodePieces(hyp_pieces.split()) res.append(hyp_words) return res @@ -96,7 +97,8 @@ def calcMN(features): def transcribe(waveform, args, task, generator, models, sp, tgt_dict): num_features = 80 output = torchaudio.compliance.kaldi.fbank(waveform, num_mel_bins=num_features) - output_cmvn = calcMN(output.cpu().detach()) + device = torch.device("cuda:0" if torch.cuda.is_available() and not args.cpu else "cpu") + output_cmvn = calcMN(output.to(device).detach()) # size (m, n) source = output_cmvn @@ -151,7 +153,7 @@ def setup_asr(args, logger): optimize_models(args, use_cuda, models) # Initialize generator - generator = task.build_generator(args) + generator = task.build_generator(models, args) sp = spm.SentencePieceProcessor() sp.Load(os.path.join(args.data, "spm.model")) diff --git a/packaging/pkg_helpers.bash b/packaging/pkg_helpers.bash index 3ec49a0269..57b23b93d3 100644 --- a/packaging/pkg_helpers.bash +++ b/packaging/pkg_helpers.bash @@ -32,7 +32,7 @@ setup_cuda() { # First, compute version suffixes. By default, assume no version suffixes export VERSION_SUFFIX="" export PYTORCH_VERSION_SUFFIX="" - export WHEEL_DIR="" + export WHEEL_DIR="cpu/" # Wheel builds need suffixes (but not if they're on OS X, which never has suffix) if [[ "$BUILD_TYPE" == "wheel" ]] && [[ "$(uname)" != Darwin ]]; then # The default CUDA has no suffix diff --git a/test/torchaudio_unittest/datasets/tedlium_test.py b/test/torchaudio_unittest/datasets/tedlium_test.py new file mode 100644 index 0000000000..c19984cac2 --- /dev/null +++ b/test/torchaudio_unittest/datasets/tedlium_test.py @@ -0,0 +1,153 @@ +import os + +from torchaudio.datasets import tedlium + +from torchaudio_unittest.common_utils import ( + TempDirMixin, + TorchaudioTestCase, + get_whitenoise, + save_wav, + normalize_wav, +) + +# Used to generate a unique utterance for each dummy audio file +UTTERANCES = [ + "AaronHuey_2010X 1 AaronHuey_2010X 0.0 2.0 script1\n", + "AaronHuey_2010X 1 AaronHuey_2010X 2.0 4.0 script2\n", + "AaronHuey_2010X 1 AaronHuey_2010X 4.0 6.0 script3\n", + "AaronHuey_2010X 1 AaronHuey_2010X 6.0 8.0 script4\n", + "AaronHuey_2010X 1 AaronHuey_2010X 8.0 10.0 script5\n", +] + +PHONEME = [ + "a AH", + "a(2) EY", + "aachen AA K AH N", + "aad AE D", + "aaden EY D AH N", + "aadmi AE D M IY", + "aae EY EY", +] + + +class TestTedlium(TempDirMixin, TorchaudioTestCase): + backend = "default" + + root_dir = None + samples = {} + + @classmethod + def setUpClass(cls): + cls.root_dir = cls.get_base_temp_dir() + cls.root_dir = dataset_dir = os.path.join(cls.root_dir, "tedlium") + os.makedirs(dataset_dir, exist_ok=True) + sample_rate = 16000 # 16kHz + seed = 0 + + for release in ["release1", "release2", "release3"]: + data = get_whitenoise(sample_rate=sample_rate, duration=10.00, n_channels=1, dtype="float32", seed=seed) + if release in ["release1", "release2"]: + release_dir = os.path.join( + dataset_dir, + tedlium._RELEASE_CONFIGS[release]["folder_in_archive"], + tedlium._RELEASE_CONFIGS[release]["subset"], + ) + else: + release_dir = os.path.join( + dataset_dir, + tedlium._RELEASE_CONFIGS[release]["folder_in_archive"], + tedlium._RELEASE_CONFIGS[release]["data_path"], + ) + os.makedirs(release_dir, exist_ok=True) + os.makedirs(os.path.join(release_dir, "stm"), exist_ok=True) # Subfolder for transcripts + os.makedirs(os.path.join(release_dir, "sph"), exist_ok=True) # Subfolder for audio files + filename = f"{release}.sph" + path = os.path.join(os.path.join(release_dir, "sph"), filename) + save_wav(path, data, sample_rate) + + trans_filename = f"{release}.stm" + trans_path = os.path.join(os.path.join(release_dir, "stm"), trans_filename) + with open(trans_path, "w") as f: + f.write("".join(UTTERANCES)) + + dict_filename = f"{release}.dic" + dict_path = os.path.join(release_dir, dict_filename) + with open(dict_path, "w") as f: + f.write("\n".join(PHONEME)) + + # Create a samples list to compare with + cls.samples[release] = [] + for utterance in UTTERANCES: + talk_id, _, speaker_id, start_time, end_time, identifier, transcript = utterance.split(" ", 6) + start_time = int(float(start_time)) * sample_rate + end_time = int(float(end_time)) * sample_rate + sample = ( + data[:, start_time:end_time], + sample_rate, + transcript, + talk_id, + speaker_id, + identifier, + ) + cls.samples[release].append(sample) + seed += 1 + + def test_tedlium_release1(self): + release = "release1" + dataset = tedlium.TEDLIUM(self.root_dir, release=release) + num_samples = 0 + for i, (data, sample_rate, transcript, talk_id, speaker_id, identifier) in enumerate(dataset): + self.assertEqual(data, self.samples[release][i][0], atol=5e-5, rtol=1e-8) + assert sample_rate == self.samples[release][i][1] + assert transcript == self.samples[release][i][2] + assert talk_id == self.samples[release][i][3] + assert speaker_id == self.samples[release][i][4] + assert identifier == self.samples[release][i][5] + num_samples += 1 + + assert num_samples == len(self.samples[release]) + + dataset._dict_path = os.path.join(dataset._path, f"{release}.dic") + phoneme_dict = dataset.phoneme_dict + phoenemes = [f"{key} {' '.join(value)}" for key, value in phoneme_dict.items()] + assert phoenemes == PHONEME + + def test_tedlium_release2(self): + release = "release2" + dataset = tedlium.TEDLIUM(self.root_dir, release=release) + num_samples = 0 + for i, (data, sample_rate, transcript, talk_id, speaker_id, identifier) in enumerate(dataset): + self.assertEqual(data, self.samples[release][i][0], atol=5e-5, rtol=1e-8) + assert sample_rate == self.samples[release][i][1] + assert transcript == self.samples[release][i][2] + assert talk_id == self.samples[release][i][3] + assert speaker_id == self.samples[release][i][4] + assert identifier == self.samples[release][i][5] + num_samples += 1 + + assert num_samples == len(self.samples[release]) + + dataset._dict_path = os.path.join(dataset._path, f"{release}.dic") + phoneme_dict = dataset.phoneme_dict + phoenemes = [f"{key} {' '.join(value)}" for key, value in phoneme_dict.items()] + assert phoenemes == PHONEME + + def test_tedlium_release3(self): + release = "release3" + dataset = tedlium.TEDLIUM(self.root_dir, release=release) + num_samples = 0 + for i, (data, sample_rate, transcript, talk_id, speaker_id, identifier) in enumerate(dataset): + self.assertEqual(data, self.samples[release][i][0], atol=5e-5, rtol=1e-8) + assert sample_rate == self.samples[release][i][1] + assert transcript == self.samples[release][i][2] + assert talk_id == self.samples[release][i][3] + assert speaker_id == self.samples[release][i][4] + assert identifier == self.samples[release][i][5] + num_samples += 1 + + assert num_samples == len(self.samples[release]) + + dataset._dict_path = os.path.join(dataset._path, f"{release}.dic") + phoneme_dict = dataset.phoneme_dict + phoenemes = [f"{key} {' '.join(value)}" for key, value in phoneme_dict.items()] + assert phoenemes == PHONEME diff --git a/test/torchaudio_unittest/functional/functional_cpu_test.py b/test/torchaudio_unittest/functional/functional_cpu_test.py index 96aac4e770..4fbe4d871e 100644 --- a/test/torchaudio_unittest/functional/functional_cpu_test.py +++ b/test/torchaudio_unittest/functional/functional_cpu_test.py @@ -21,6 +21,23 @@ class TestLFilterFloat64(Lfilter, common_utils.PytorchTestCase): device = torch.device('cpu') +class TestCreateFBMatrix(common_utils.TorchaudioTestCase): + def test_no_warning_high_n_freq(self): + with pytest.warns(None) as w: + F.create_fb_matrix(288, 0, 8000, 128, 16000) + assert len(w) == 0 + + def test_no_warning_low_n_mels(self): + with pytest.warns(None) as w: + F.create_fb_matrix(201, 0, 8000, 89, 16000) + assert len(w) == 0 + + def test_warning(self): + with pytest.warns(None) as w: + F.create_fb_matrix(201, 0, 8000, 128, 16000) + assert len(w) == 1 + + class TestComputeDeltas(common_utils.TorchaudioTestCase): """Test suite for correctness of compute_deltas""" def test_one_channel(self): diff --git a/test/torchaudio_unittest/sox_io_backend/save_test.py b/test/torchaudio_unittest/sox_io_backend/save_test.py index f7d4da3931..deed1aa1ad 100644 --- a/test/torchaudio_unittest/sox_io_backend/save_test.py +++ b/test/torchaudio_unittest/sox_io_backend/save_test.py @@ -254,7 +254,7 @@ def test_mp3_large(self, sample_rate, num_channels, bit_rate): @parameterized.expand(list(itertools.product( [8000, 16000], [1, 2], - list(range(9)), + [None] + list(range(9)), )), name_func=name_func) def test_flac(self, sample_rate, num_channels, compression_level): """`sox_io_backend.save` can save flac format.""" @@ -273,7 +273,7 @@ def test_flac_large(self, sample_rate, num_channels, compression_level): @parameterized.expand(list(itertools.product( [8000, 16000], [1, 2], - [-1, 0, 1, 2, 3, 3.6, 5, 10], + [None, -1, 0, 1, 2, 3, 3.6, 5, 10], )), name_func=name_func) def test_vorbis(self, sample_rate, num_channels, quality_level): """`sox_io_backend.save` can save vorbis format.""" diff --git a/torchaudio/backend/soundfile_backend.py b/torchaudio/backend/soundfile_backend.py index 680264bcd6..49cada21b0 100644 --- a/torchaudio/backend/soundfile_backend.py +++ b/torchaudio/backend/soundfile_backend.py @@ -73,6 +73,7 @@ def load(filepath: str, @_mod_utils.requires_module('soundfile') +@_mod_utils.deprecated('Please use "torchaudio.load".', '0.9.0') @common._impl_load_wav def load_wav(filepath, **kwargs): kwargs['normalization'] = 1 << 16 diff --git a/torchaudio/backend/sox_backend.py b/torchaudio/backend/sox_backend.py index 4e2b118f8b..45d75817af 100644 --- a/torchaudio/backend/sox_backend.py +++ b/torchaudio/backend/sox_backend.py @@ -63,6 +63,7 @@ def load(filepath: str, @_mod_utils.requires_module('torchaudio._torchaudio') +@_mod_utils.deprecated('Please use "torchaudio.load".', '0.9.0') @common._impl_load_wav def load_wav(filepath, **kwargs): kwargs['normalization'] = 1 << 16 diff --git a/torchaudio/backend/sox_io_backend.py b/torchaudio/backend/sox_io_backend.py index 9c0695d723..e2823ada00 100644 --- a/torchaudio/backend/sox_io_backend.py +++ b/torchaudio/backend/sox_io_backend.py @@ -24,11 +24,15 @@ def info(filepath: str) -> AudioMetaData: """Get signal information of an audio file. Args: - filepath (str): Path to audio file + filepath (str or pathlib.Path): + Path to audio file. This function also handles ``pathlib.Path`` objects, but is annotated as + ``str`` for TorchScript compiler compatibility. Returns: AudioMetaData: meta data of the given audio. """ + # Cast to str in case type is `pathlib.Path` + filepath = str(filepath) sinfo = torch.ops.torchaudio.sox_io_get_info(filepath) return AudioMetaData(sinfo.get_sample_rate(), sinfo.get_num_frames(), sinfo.get_num_channels()) @@ -80,8 +84,9 @@ def load( ``[-1.0, 1.0]``. Args: - filepath (str): - Path to audio file + filepath (str or pathlib.Path): + Path to audio file. This function also handles ``pathlib.Path`` objects, but is + annotated as ``str`` for TorchScript compiler compatibility. frame_offset (int): Number of frames to skip before start reading data. num_frames (int): @@ -105,6 +110,8 @@ def load( integer type, else ``float32`` type. If ``channels_first=True``, it has ``[channel, time]`` else ``[time, channel]``. """ + # Cast to str in case type is `pathlib.Path` + filepath = str(filepath) signal = torch.ops.torchaudio.sox_io_load_audio_file( filepath, frame_offset, num_frames, normalize, channels_first) return signal.get_tensor(), signal.get_sample_rate() @@ -140,7 +147,9 @@ def save( and corresponding codec libraries such as ``libmad`` or ``libmp3lame`` etc. Args: - filepath (str): Path to save file. + filepath (str or pathlib.Path): + Path to save file. This function also handles ``pathlib.Path`` objects, but is annotated + as ``str`` for TorchScript compiler compatibility. tensor (torch.Tensor): Audio data to save. must be 2D tensor. sample_rate (int): sampling rate channels_first (bool): @@ -158,8 +167,10 @@ def save( See the detail at http://sox.sourceforge.net/soxformat.html. """ + # Cast to str in case type is `pathlib.Path` + filepath = str(filepath) if compression is None: - ext = str(filepath)[-3:].lower() + ext = str(filepath).split('.')[-1].lower() if ext in ['wav', 'sph']: compression = 0. elif ext == 'mp3': @@ -175,6 +186,7 @@ def save( @_mod_utils.requires_module('torchaudio._torchaudio') +@_mod_utils.deprecated('Please use "torchaudio.load".', '0.9.0') def load_wav( filepath: str, frame_offset: int = 0, diff --git a/torchaudio/backend/utils.py b/torchaudio/backend/utils.py index 84ec2fa3f5..87ec948fba 100644 --- a/torchaudio/backend/utils.py +++ b/torchaudio/backend/utils.py @@ -49,6 +49,11 @@ def set_audio_backend(backend: Optional[str]): if backend is None: module = no_backend elif backend == 'sox': + warnings.warn( + '"sox" backend is being deprecated. ' + 'The default backend will be changed to "sox_io" backend in 0.8.0 and ' + '"sox" backend will be removed in 0.9.0. Please migrate to "sox_io" backend. ' + 'Please refer to https://github.com/pytorch/audio/issues/903 for the detail.') module = sox_backend elif backend == 'sox_io': module = sox_io_backend diff --git a/torchaudio/datasets/__init__.py b/torchaudio/datasets/__init__.py index fb9e0db506..5e5b9c1118 100644 --- a/torchaudio/datasets/__init__.py +++ b/torchaudio/datasets/__init__.py @@ -8,6 +8,7 @@ from .ljspeech import LJSPEECH from .cmuarctic import CMUARCTIC from .libritts import LIBRITTS +from .tedlium import TEDLIUM __all__ = ( "COMMONVOICE", @@ -19,7 +20,8 @@ "LJSPEECH", "GTZAN", "CMUARCTIC", - "LIBRITTS" + "LIBRITTS", "diskcache_iterator", "bg_iterator", + "TEDLIUM", ) diff --git a/torchaudio/datasets/tedlium.py b/torchaudio/datasets/tedlium.py new file mode 100644 index 0000000000..4ae1ddeb0f --- /dev/null +++ b/torchaudio/datasets/tedlium.py @@ -0,0 +1,208 @@ +import os +from typing import Tuple + +import torchaudio +from torch import Tensor +from torch.utils.data import Dataset +from torchaudio.datasets.utils import ( + download_url, + extract_archive, +) + + +_RELEASE_CONFIGS = { + "release1": { + "folder_in_archive": "TEDLIUM_release1", + "url": "http://www.openslr.org/resources/7/TEDLIUM_release1.tar.gz", + "checksum": "30301975fd8c5cac4040c261c0852f57cfa8adbbad2ce78e77e4986957445f27", + "data_path": "", + "subset": "train", + "supported_subsets": ["train", "test", "dev"], + "dict": "TEDLIUM.150K.dic", + }, + "release2": { + "folder_in_archive": "TEDLIUM_release2", + "url": "http://www.openslr.org/resources/19/TEDLIUM_release2.tar.gz", + "checksum": "93281b5fcaaae5c88671c9d000b443cb3c7ea3499ad12010b3934ca41a7b9c58", + "data_path": "", + "subset": "train", + "supported_subsets": ["train", "test", "dev"], + "dict": "TEDLIUM.152k.dic", + }, + "release3": { + "folder_in_archive": "TEDLIUM_release-3", + "url": "http://www.openslr.org/resources/51/TEDLIUM_release-3.tgz", + "checksum": "ad1e454d14d1ad550bc2564c462d87c7a7ec83d4dc2b9210f22ab4973b9eccdb", + "data_path": "data/", + "subset": None, + "supported_subsets": [None], + "dict": "TEDLIUM.152k.dic", + }, +} + + +class TEDLIUM(Dataset): + """ + Create a Dataset for Tedlium. It supports releases 1,2 and 3, each item is a list containings: + [waveform, sample_rate, transcript, talk_id, speaker_id, identifier]. + + Constructor arguments: + + Args: + root (str): Path containing dataset or target path where its downloaded if needed + release (str, optional): TEDLIUM identifier (release1,release2,release3). Defaults to RELEASE. + subset (str, optional): train/dev/test for releases 1&2, None for release3. Defaults to Train/None + download (bool, optional): Download dataset in case is not founded in root path. Defaults to False. + audio_ext (str, optional): Overwrite audio extension when loading items. Defaults to ".sph". + + Special functions: + + _load_tedlium_item: Loads a TEDLIUM dataset sample given a file name and corresponding sentence name + + _load_audio: Default load function used in TEDLIUM dataset, you can overwrite this function to customize + functionality and load individual sentences from a full ted audio talk file + + get_phoneme_dict: Returns the phoneme dictionary of a TEDLIUM release + + """ + + def __init__( + self, root: str, release: str = "release1", subset: str = None, download: bool = False, audio_ext=".sph" + ) -> None: + """Constructor for TEDLIUM dataset. + + Args: + root (str): Path containing dataset or target path where its downloaded if needed + release (str, optional): TEDLIUM identifier (release1,release2,release3). Defaults to RELEASE. + subset (str, optional): train/dev/test for releases 1&2, None for release3. Defaults to Train/None + download (bool, optional): Download dataset in case is not founded in root path. Defaults to False. + audio_ext (str, optional): Overwrite audio extension when loading items. Defaults to ".sph". + + Raises: + RuntimeError: If release identifier does not match any supported release, + """ + self._ext_audio = audio_ext + if release in _RELEASE_CONFIGS.keys(): + folder_in_archive = _RELEASE_CONFIGS[release]["folder_in_archive"] + url = _RELEASE_CONFIGS[release]["url"] + subset = subset if subset else _RELEASE_CONFIGS[release]["subset"] + else: + # Raise warning + raise RuntimeError( + "The release {} does not match any of the supported tedlium releases{} ".format( + release, _RELEASE_CONFIGS.keys(), + ) + ) + if subset not in _RELEASE_CONFIGS[release]["supported_subsets"]: + # Raise warning + raise RuntimeError( + "The subset {} does not match any of the supported tedlium subsets{} ".format( + subset, _RELEASE_CONFIGS[release]["supported_subsets"], + ) + ) + + basename = os.path.basename(url) + archive = os.path.join(root, basename) + + basename = basename.split(".")[0] + + self._path = os.path.join(root, folder_in_archive, _RELEASE_CONFIGS[release]["data_path"]) + if subset in ["train", "dev", "test"]: + self._path = os.path.join(self._path, subset) + + if download: + if not os.path.isdir(self._path): + if not os.path.isfile(archive): + checksum = _RELEASE_CONFIGS[release]["checksum"] + download_url(url, root, hash_value=checksum) + extract_archive(archive) + + # Create list for all samples + self._filelist = [] + stm_path = os.path.join(self._path, "stm") + for file in sorted(os.listdir(stm_path)): + if file.endswith(".stm"): + stm_path = os.path.join(self._path, "stm", file) + with open(stm_path) as f: + l = len(f.readlines()) + file = file.replace(".stm", "") + self._filelist.extend((file, line) for line in range(l)) + # Create dict path for later read + self._dict_path = os.path.join(root, folder_in_archive, _RELEASE_CONFIGS[release]["dict"]) + self._phoneme_dict = None + + def _load_tedlium_item(self, fileid: str, line: int, path: str) -> Tuple[Tensor, int, str, int, int, int]: + """Loads a TEDLIUM dataset sample given a file name and corresponding sentence name. + + Args: + fileid (str): File id to identify both text and audio files corresponding to the sample + line (int): Line identifier for the sample inside the text file + path (str): Dataset root path + + Returns: + Tedlium_item: A namedTuple containing [waveform, sample_rate, transcript, talk_id, speaker_id, identifier] + """ + transcript_path = os.path.join(path, "stm", fileid) + with open(transcript_path + ".stm") as f: + transcript = f.readlines()[line] + talk_id, _, speaker_id, start_time, end_time, identifier, transcript = transcript.split(" ", 6) + + wave_path = os.path.join(path, "sph", fileid) + waveform, sample_rate = self._load_audio(wave_path + self._ext_audio, start_time=start_time, end_time=end_time) + + return (waveform, sample_rate, transcript, talk_id, speaker_id, identifier) + + def _load_audio(self, path: str, start_time: float, end_time: float, sample_rate: int = 16000) -> [Tensor, int]: + """Default load function used in TEDLIUM dataset, you can overwrite this function to customize functionality + and load individual sentences from a full ted audio talk file. + + Args: + path (str): Path to audio file + start_time (int, optional): Time in seconds where the sample sentence stars + end_time (int, optional): Time in seconds where the sample sentence finishes + + Returns: + [Tensor, int]: Audio tensor representation and sample rate + """ + start_time = int(float(start_time) * sample_rate) + end_time = int(float(end_time) * sample_rate) + if torchaudio.get_audio_backend() == "sox_io": + return torchaudio.load(path, frame_offset=start_time, num_frames=end_time - start_time) + return torchaudio.load(path)[:, start_time:end_time] + + def __getitem__(self, n: int) -> Tuple[Tensor, int, str, int, int, int]: + """TEDLIUM dataset custom function overwritting default loadbehaviour + Loads a TEDLIUM sample given a index N. + + Args: + n (int): Index of sample to be loaded + + Returns: + Tedlium_item: A namedTuple containing [waveform, sample_rate, transcript, talk_id, speaker_id, identifier] + """ + fileid, line = self._filelist[n] + return self._load_tedlium_item(fileid, line, self._path) + + def __len__(self) -> int: + """TEDLIUM dataset custom function overwritting len default behaviour. + + Returns: + int: TEDLIUM dataset length + """ + return len(self._filelist) + + @property + def phoneme_dict(self): + """Returns the phoneme dictionary of a TEDLIUM release. + + Returns: + dictionary: Phoneme dictionary for the current tedlium release + """ + # Read phoneme dictionary + if not self._phoneme_dict: + self._phoneme_dict = {} + with open(self._dict_path, "r", encoding="utf-8") as f: + for line in f.readlines(): + content = line.strip().split() + self._phoneme_dict[content[0]] = tuple(content[1:]) # content[1:] can be empty list + return self._phoneme_dict.copy() diff --git a/torchaudio/functional.py b/torchaudio/functional.py index 4e8e62848e..a6514c79f2 100644 --- a/torchaudio/functional.py +++ b/torchaudio/functional.py @@ -313,6 +313,13 @@ def create_fb_matrix( enorm = 2.0 / (f_pts[2:n_mels + 2] - f_pts[:n_mels]) fb *= enorm.unsqueeze(0) + if (fb.max(dim=0).values == 0.).any(): + warnings.warn( + "At least one mel filterbank has all zero values. " + f"The value for `n_mels` ({n_mels}) may be set too high. " + f"Or, the value for `n_freqs` ({n_freqs}) may be set too low." + ) + return fb From f4611b75da718bbc312b55e5262127bd397059f8 Mon Sep 17 00:00:00 2001 From: Alexei Baevski Date: Tue, 6 Oct 2020 16:59:20 -0700 Subject: [PATCH 19/19] replace max-sentences with batch-size for dependencies Summary: this fixes some regressions introduced by D24121305. fairseq configuration is changing from command line to dataclasses (via hydra eventually) which no longer supports option aliases. one such alias is --max-sentences / --batch-size, and D24121305 removed --max-sentences as --batch-size is more appropriate (fairseq is not just an nlp framework dealing with sentences). unfortunately it seems some existing flows broke and this diff attempts to fix this Differential Revision: D24142488 fbshipit-source-id: 075180ea10a9d706a3f8d64b978d66dfd83c3d2b --- examples/interactive_asr/utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/interactive_asr/utils.py b/examples/interactive_asr/utils.py index 37f5ae5303..3aae6c6864 100644 --- a/examples/interactive_asr/utils.py +++ b/examples/interactive_asr/utils.py @@ -124,7 +124,7 @@ def setup_asr(args, logger): check_args(args) import_user_module(args) - if args.max_tokens is None and args.max_sentences is None: + if args.max_tokens is None and args.batch_size is None: args.max_tokens = 30000 logger.info(args)

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